SMARTraj$^2$: A Stable Multi-City Adaptive Method for Multi-View Spatio-Temporal Trajectory Representation Learning
Authors: Tangwen Qian
Junhe Li
Yile Chen
Gao Cong
Zezhi Shao
Jun Zhang
Tao Sun
Fei Wang
Yongjun Xu
Venue: NeurIPS 2025 poster ()
Keywords: Trajectory Representation Learning
Spatio-Temporal Data Mining
Self-Supervised Learning
Primary area: applications

Abstract: Spatio-temporal trajectory representation learning plays a crucial role in various urban applications such as transportation systems, urban planning, and environmental monitoring. Existing methods can be divided into single-view and multi-view approaches, with the latter offering richer representations by integrating multiple sources of spatio-temporal data. However, these methods often struggle to generalize across diverse urban scenes due to multi-city structural heterogeneity, which arises from the disparities in road networks, grid layouts, and traffic regulations across cities, and the amplified seesaw phenomenon, where optimizing for one city, view, or task can degrade performance in others. These challenges hinder the deployment of trajectory learning models across multiple cities, limiting their real-world applicability. In this work, we propose SMARTraj$^2$, a novel stable multi-city adaptive method for multi-view spatio-temporal trajectory representation learning. Specifically, we introduce a feature disentanglement module to separate domain-invariant and domain-specific features, and a personalized gating mechanism to dynamically stabilize the contributions of different views and tasks. Our approach achieves superior generalization across heterogeneous urban scenes while maintaining robust performance across multiple downstream tasks. Extensive experiments on benchmark datasets demonstrate the effectiveness of SMARTraj$^2$ in enhancing cross-city generalization and outperforming state-of-the-art methods. See our project website at \url{https://github.com/GestaltCogTeam/SMARTraj}.

SMARTraj?: A Stable Multi-City Adaptive Method for Multi- View Spatio-Temporal Trajectory Representation Learning
Tangwen Qian'-3 Junhe Li Yile Chen²", Gao Cong", Zezhi Shao'.3,
Jun Zhang! Tao Sun Fei Wang Yongjun Xul.3
State Key Laboratory of Al Safety, Institute of Computing Technology, Chinese Academy of Sciences
Coolegee of Computing and Data Science, Nanyang Technological University University Chinese Academy Sciences
fqiantangven, shaozezhi, suntao, wangfei, x xyjj@ict.aa cn, s1 jhhy@ggaiil.... com, yile0010e ntu, edu. gaocongentu, edu. zhangjun2540m ucas ac on
shaaatzangeen,saazzzzh,,
Abstract
Spatio-temporal trajectory representation learning plays a crucial role in various urban applications such transportation systems, urban planning, and environmen- tal monitoring. Existing methods can be divided into single-view and multi-vied approaches, with the latter offering richer representations by integrating multiple sources of spatio-temporal data. However, these methods often struggle to general- ize across diverse urban scenes due multi-city structural heterogeneity, which arises from the disparities in road networks, grid layouts, and traffic regulations across cities, and the amplified seesaw phenomenon, where optimizing for one city, view, or task can degrade performance in others. These challenges hinder the deployment of trajectory learning models across multiple cities, limiting their real- world applicability. In this work, we propose SMARTraj², a novel stable multi-city adaptive method for multi-view apatio-tempont trajectory representation learning. Specifically, we introduce feature disentanglement module to separate domain- invariant and domain-speen speccifc features, and personalized gating mechanism dynamically stabilize the contributions of different views and tasks. Our approach achieves superior generalization across heterogeneous urban scenes while maintain- ing robust performance across multiple downstream tasks. Extensive experiments on benchmark datasets demonstrate the effectiveness of SMARTraj² in enhanc- ing cross-city generalization and outperforming state-of-the-art methods. See our project website hhtts:://// :/gi:t:///iii //github.. com Caamm/Getallttaoo hctmm//estaalltoooeeee////MMAA
Introduction
Spatio-temporal trajectory representation learning is fundamental to variety of urban applications,
including intelligent transportation systems [52 28), urban planning [9] [46], and environmental monitoring [18 44). The goal is to encode apatio-tempore data (e.g., GPS coordinates, road networks, timestamps, and points of interest) into representations that capture the underlying patterns of urban
mobility, facilitating diverse downstream tasks such anomaly detection 41) clustering 43
29
and trajectory forecasting 48 35
Current methods can be divided into two subcategories: single-view and multi-view approaches. Single-view methods leverage one specific type of spatial data, such as GPS trajectories 23 20)
"Corresoodingg Authors: Yile Chen, Tao Sun.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

Although these methods effectively capture patterns within respective modalities, their reliance on a single view inherently limits ability to model the complex and multi-faceted nature of urban mobility. In contrast, multi-view approaches 24 33 aim to enhance representation richness by integrating multiple types of spatio- temporal data, enabling a more comprehensive understanding of mobility behaviors. However, these methods are often constrained to datasets from a single city, significantly limiting their generalization capability to other urban scenes with distinct characteristics. Generalization across cities is crucial, as urban scenes exhibit considerable diversity in geography, infrastructure, and human mobility patterns. Methods lacking the ability to generalize across cities struggle to maintain robustness in real-world applications, where models need to adapt to diverse and unseen urban contexts. Thus, achieving generalization across cities is essential for stable and transferable representation learning.
road network routes [1] 13 or points of interest (POI) sequences 40 30].
trajectory
Multi-city Structural
Route
Routo Embedding
Routo Embedding
Route View
Heterogeneity
Route View Misalignment
City
00000000 0000
Grid Embedding
Embedding
Gridd View
Grid View
Grid View
Misalignment
Model
Downstream Tasks Performance
A
1
3
City A
City B
City
City
BOTA ( Road Label Classiflcation 2
Destination Road Prediction (3) Destiiation Grid Predictoon 3 Travel Time Extimation
Seesaw Phenomenon
A
A
Figure 1: Existing multi-view spatio-temporal trajectory representation learning methods face critical challenges in generalizing across cities, which is crucial for real-world applications with diverse urban scenes: (1) multi-city structural heterogeneity, arising from disparities in urban layouts, and (2) the amplified seesaw phenomenon, where performance trade-offs between cities, views, and tasks are more pronounced.
To address this limitation, our goal is to enable spatio-temporal trajectory representation learning that generalizes across diverse urban scenes, which requires capturing universal spatio-temporal patterns shared across cities while also preserving city-specific characteristics account for unique urban features. To achieve this, we propose a novel multi-city adaptive method that leverages multi-view spatio-temporal data to learn stable representations. However, realizing this goal introduces two critical challenges:
The first challenge is multi-city structural heterogeneity. Urban scenes exhibit significant disparities in structural layouts, such as road networks, grid partitions, and traffic regulations, which lead to distinct spatio-temporal patterns across cities. These differences make it difficult to develop model that generalizes across diverse urban landscapes. Specifically, spaces for corresponding views (e.g.. route and grid views) across cities are inherently disjoint. For instance, road ID embeddings in one city operate in a different embedding space from those in another city, and grid ID embeddings often encode distinct spatial structures. This lack of alignment among embedding spaces prevents consistent representation learning, limiting the model's ability to generalize across cities.
multi-view settings, embedding
The second challenge is the amplified seesaw phenomenon. task-agnostic representation learning, balancing performance across multiple downstream tasks is critical. However, this balance is

---

inherently difficult in multi-view settings, where optimizing for one view often leads to performance degradation others due to the heterogeneous nature of data across views. The challenge becomes even more complex in multi-city scenes. Specifically, multi-city, multi-view trajectory data introduces additional heterogeneity, as each city exhibits unique data distributions and structural characteristics. This amplifies the seesaw phenomenon, where improvements in performance for one city, view, or task may disproportionately degrade performance in others. Furthermore, achieving generalization while supporting multiple downstream tasks significantly complicates the learning process, making it challenging to maintain stable performance across cities, views, and tasks.
To tackle these challenges, we introduce SMARTraj". Stable Multi-city Adaptive method for Multi-view spatio-temporal Trajectory representation learning. To address the multi-city structural heterogeneity, we design feature disentanglement module that separates domain-invariane and Nomain-specinf features using orthogonality constraints, ensuring the model captures generalized spatio-temporal patterns while preserving city-specific characteristics. This disentanglement allows the model to adapt new cities without losing critical information specific to the local urban structure. To mitigate the amplified seesaw phenomenon, we develop personalized gating mechanism that dynamically adjusts the contributions of domain-invarian and domain specific representations. The gating mechanism operates at both city-level and trajectory-level, adapting the contributions for different cities, views, and tasks. This ensures robust performance across cities while minimizing degradation in any specific view task. By integrating them, SMARTraj" effectively stabilizes the trade-offss among cities, views, and tasks, enabling generalization across heterogeneous urban scenes.
The contributions of this work are summarized as follows:
To the best of our knowledge, this is the first work to highlight the importance of multi-city, multi-view trajectory representation learning with a focus on generalization across diverse urban scenes. To improve this, we propose novel method, SMARTraj², designed to learn stable representations from heterogeneous spatio-temporal data.
We design feature disentanglement module to separate domain-invariane and domain- specific representations, ensuring effective generalization while preserving city-specific characteristics. Additionally, a personalized gating mechanism is introduced to dynamically stabilize the contributions of different views and tasks, mitigating the amplified seesaw phenomenon in multi-city, multi-task settings.
Extensive experiments demonstrate the superior performance of SMARTraj² compared to sfat-oo-the--ae methods, validating its stability in handling heterogeneous spatio-tempone data from various urban contexts.
2 Preliminaries
In this section, we introduce the fundamental concepts and formally define the problem addressed in this paper.
Definition 1. (Trajectory). A trajectory T of length [T'] sequence of spatial and temporal data points, denoted as T = {poos, +=)) where pos, represents the spatial location of the i-th sampled point (e.g., road segment ID, grid cell index, or exact latitude and longitude), and the corresponding timestamp.
A trajectory can be represented in multiple ways, each capturing distinct spatial and contextual aspects of the underlying movement. Specifically, a multi-view trajectory representation integrates several spatial views, with each view offering unique insights into the underlying trajectory. These views include:
GPS View TP: A high-resolution view of the trajectory consisting of raw geographic coordinates, i.e., pos; (lat,, lon;), where lat, and lon; are the exact latitude and longitude of the i-th point.
Route View T": A structural view of the trajectory aligned with the road network, incor- porating road segments and intersections, i.e., pos, = Vi, where vs the ID of the road segment associated with the i-th point.
3

---

Grid View 7 A macro-level view of the trajectory representing movements across spatial grid, which may be augmented with semantic information, such as points interest (POIs). Specifically, pos; grid, where grid, is the index of the grid cell containing the i-th point.
Definition 2. (Multi- Spatio-Tempon Trajectory Representation Learning). Given multi- view trajectory dataset T {(T,, T. T the objective of multi-view spatio-temporal trajectory representation learning is to learn robust, task-agnostic representations for different views. These representations should generalize across various downstream tasks, such road label classification, travel time estimation, and destination grid prediction.
Building on the previous definitions, we formally define the problem addressed in this paper.
Problem Statement. Let = {Di. D2....... ..... D p) be a dataset consisting of multi-view trajectories collected from multiple cities. For each city k; the dataset Dj. is composed of multi-view trajectory dataset D; [ where each trajectory T, = (T,", ; T", T views, GPS view T, route view TÇ. and grid view TV- The goal is to lear transferable trajectory representations that integrate spatial information from these different views, while adapting to the unique characteristies of each city. The learned representations should enable stable performance across various downstream tasks by generalizing effectively across diverse urban scenes.
tuple containing the three
Method
In this section, we introduce the architecture of SMARTraj², detailing its core components: the feature disentanglement module and the personalized gating mechanism, designed to address the challenges posed by multi-city structural heterogeneity and mitigate the amplified seesaw phenomenon often observed in urban trajectory data.
3.1 Overview
cityaid Ieat ren
Enjedtory-sdd de
Feature Disentanglement Module
fy
Ad
Dase
Ager
K27
domain
Git-itoue
Individual Extractor
Apu AAAA
GPS-Gr Vie
BEUC
Ris
DaS0
hg c009
View
chyspecllic
Shared Weight Extractor
domain invaniant Li
Personalized Gating
Mechanism
OlN Werk
Vinwr
Dase
Li
OPS-Cot
ODDS
Extractor
Individual
domain -op--iiiic features
excspese
(B883
BBBG
G002
View
Olyyakde fealurig
Tnpuecorysade
ianturee
Various view based encoders
DECO
Trajectory Repr esootatioon
Grid/GPS-Gr Gra3PP Route-Rouie Views Erbbodding W thour, g gradient backgropopation
Lyoh
Contraativ Leernnng Loss
Masked Language Moxiding Losa
Tupyy
Diflenenge Loss
pate
Element--eioe
Figure 2: An overview of SMARTraj², consisting of two main components: the feature disentangle- ment module and the personalized gating mechanism.
As illustrated in Fig. 2 SMARTraj2 comprises two key modules: (1) the feature disentanglement module separates domain-invariant and domain-speciffe features, utilizing orthogonality constraints to ensure the model captures generalized spatio-temporal patterns while maintaining city-specific characteristics. This disentanglement allows for flexible adaptation citics without losing

---

essential local urban information. (2) the personalized gating mechanism dynamically adjusts the contributions of
and comain-spectie features at both the city-level and trajectory- level. This ensures robust performance across cities while minimizing performance degradation in any specific view or task. This mechanism stabilizes the trade-offs between cities, views, and tasks, thereby enabling generalization across heterogeneous urban scenes.
3.2 Feature Disentanglement Module
The feature disentanglement module focuses on capturing spatial and temporal dependencies across multiple trajectory views, leveraging specialized encoders for each view. This module is pivotal for disentangling
and domain- sppccifc features, thus addressing the multi-city structural heterogeneity inherent in urban trajectory data.
For the GPS view, trajectories are first hierarchically segmented into sub-trajectories corresponding to road segments or grid cells. The GPS encoder processes these segments as follows:
hplr GPS (T BPlr)
(I)
hpi G.S (T (TPls, BPlo)
where hplr and hpig represent the encoded GPS trajectories aligned with road segments and grid cells, respectively. The hierarchical structure involves encoding individual GPS points using a bidirectional GRU, followed by encoding the resulting uub-trajectories. creating two-level architecture. The binary assignment matrices Beir and B² indicate associations between GPS points and their corresponding road segments or grid cells.
The route view incorporates spatial and temporal characteristics constrained by the road network and traffic dynamics. A graph attention network updates road segment spatial embeddings xy based on observed trajectories. Temporal features for each road segment t, combining discrete (e.g., day of the week) and continuous (c.g., travel time) variables, are added to form the final segment representation r. u by- A transformer-base based architecture then used to encode the dependencies between road segments.
hy Route Encoder (T', rr))
where h, denotes the encoded route representation.
The grid view captures spatial relationships between grid cells, integrating semantic information from Points of Interest (POIs) to reflect the functional attributes of different areas. A transformer-based encoder models these dependencies:
hg GridEnncoeer(T (TT)s
where hg is the grid trajectory representation, and (T9) represents the semantic embedding computed as weighted sum of POI category embeddings within the grid cells.
Following the principles of transfer learning [6 12 5l.
we extract domain- invariint features H'
using a shared- shared-weight extractor V:
Whp, hy) hg)
This module captures features common across cities, fostering robustness the model's generalization capabilities.
Apart from city invariant features, in the context of multi-city, the incorporation of city-specific features significantly contributes to enhancing the performance and adaptability of models across dif- ferent cities 332 54 37). While domain-invarte invariant features capture the shared underlying patterns and knowledge among various domains, domain-specific features account for the unique characteristics specific individual domains. Consequently, the extraction of specific features from trajectories in different cities becomes of paramount importance. To complement this, -peaiic features Ha are captured for each city using individual extractors Mg. which are trained separately for each city:
H My(hi, hr hg), < k < D
(5)
There exists an orthogonality constraint between domain sseciff and domain- invain-invariant fea- tures [55] 45 3]. Building upon this constraint, method integrates the difference loss Laifs via soft subspace orthogonality constraint between domain-specifie H and conainninvariant H
5

---

representations of each city to encourage a clear separation between the features related to specific domains and the features shared across domains.
Laifs
Σ
11 HEllF
(6)
where ||
.Ip denotes the squared Frohenius norm.
3.3 Personalized Gating Mechanism
The personalized gating mechanism allows the model to inject city-level and trajectory- specific information into the embedding process, enabling dynamic adaptation.
For city-level adaptation, we generate city-specific gate scores based on city-side features, such as average trajectory length and speed, using two-layer feedforward network. Specifically, we inject city-level specific personalized prior information into the embedding by using city-side features E(F (e.g., trajectory speed and statistics of trajectory length) the input. We concatenate h, with the input E a). but without using gradient backpropagation, denoted as ((-):
h max(0, W city (I,,) city)) + besty)
(7)
here, h1 represents the intermediate feature vector. (-Il-) denotes the concatenate operation. After crossing features with various prior information, we customize the generation gate scores through sigmoid function and modulate the embeddings:
8 gesty 7 a (Wiity b'itt)
(-)) denotes the sigmoid function, which is used to generate gate vectors o, and limits the output to x). < is the scaling factor that is set 2. We perform the personalized transformation embedding /,, without changing the original embedding layer, aligning features with different importance for different cities. The gate scores modulate the embeddings:
h hrity
= Scity O hy
where O denotes the element-wis wise product.
For trajectory-level adaptation, we utilize features such as POI semantics at the start and end points to further personalize the network layers. specicc gate scores are computed by concatenating the city-level gate scores with trajectory features:
h max(0, W traj (( + btraj)
(10)
we modify all DNN layer parameters by using trajectory-side features ((Ftraj) (e.g., the starting and ending points' POI semantic feature). We concat the hrity with the trajectory-side features F E(Ftraa)) as the input. To avoid affecting the embedding updated in heity, we perform the operation of stop gradient V(-) h city
gtraj = 7 (W' "raghh + B(rrs)
(11)
We use the element-wise product double and squash the hidden contributions in layer of the DNN, fully personalize DNN parameters, balancing with different sparsity for different trajecto- ries, formulated as follows. This gate are applied to adjust DNN parameters, ensuring tailored transformations:
h
traj
gtraj C citt
(12)
To further enhance representation learning, we employ two additional loss functions: the masked language modeling loss LMLM, and the contrastive learning loss Epair.
The masked language modeling loss randomly masks portions of the trajectory data, forcing the model to predict masked elements and thereby learn generalized representations of the trajectory views. Formally, this loss LaLar is defined as the negative log-likelise likelihood of correctly predicting the masked tokens:
LMLM B(TT)) log P(T Tym)
(13)

---

The contrastive learning loss distinguishes positive trajectory pairs that represent the same underlying trajectories across different views, from negative pairs that are randomly sampled.
exp(sim(HH H²))
Ppair
log (H.))P
Σ
(14)
(iiMen exp(sim H1²)
The overall training objective integrates multiple loss functions to stabilize different components:
Liotal wiCais + W2CMLM W3 CpCrr
(15)
where Cacry. LMLM. and Cpair represent the difference loss (Eq(), the masked language modeling loss (Eq [3), and the contrastive learning loss (4q W1, W2, and Wy hyperparameters introduced to adjust relative weights between them. This loss function ensures that the model learns effective representations, promoting both nomain-invariane and domain-specific adaptability.
4 Experiments
To evaluate the performance of SMARTraj", we conduct extensive experiments to answer the following research questions:
RQ1: How does SMARTraj² compare to state-of-the-ard trajectory representation learning models? (Sec. 4.2)
RQ2: What is the impact of pre-training the effectiveness of SMARTraj²? (Sec. 4.3
RQ3: How does each component of SMARTraj² contribute to its overall performance? (Sec. 4.4)
RQ4: How do hyperparameters influence the performance of SMARTraj²? (Sec. 4.5
4.1
Experimental Setup
4.1.1 Datasets
We conduct experiments on two real-world trajectory datasets from Chengdu and Xi' Xi'an, an, provided by DiDi Chuxing along with road network data from OpenStreettMaf" These datasets contain GPS trajectories collected over 15 consecutive days in the central urban areas of both cities. The first 13 days are used for training, the 14th for validation, and the 1 15th for testing.
4.1.2 Downstream Tasks and Evaluation Metrics
To assess the generalization and effectiveness of the trajectory embeddings, we evaluate performance across four distinct downstream tasks, consistent with prior studies [24 encompass both fine-gra tnee-graied (e.g., destination grid prediction) and coarse- coarse-grained grained (e.g., road label classification) aspects of trajectory modeling.
22 25 These tasks
Road Label Classification: classifies road segments into four categories: Primary, Secondary, Tertiary, and Residential. Performance measured using Micro-F1 and Macro-F1 scores.
Travel Time Estimation: predicts the travel time of trajectories across all views. Performance is evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE)
Destination Road Prediction: predicts the destination road segment of a trajectory based on its embedding derived from the route view. Performance is evaluated using top-k accuracy metrics (A which measure the proportion of times the correct destination road appears within the top-k predictions.
Destination Grid Prediction: predicts the destination grid cell a trajectory, using its embedding derived from the grid view. Performance is evaluated using Accak.
Due to space constraints, further experimental setup details provided in Appendix B.

---

Table 1: Overall Performance in Xi'an.
Road Label
Travel Time MAE/ RMSE
Destination Road
Destination Grid
Method
Micro-F1 / Macro-FI
Acc@ 1 A
Acc@ 1 Acc
Random
0,4680 / 0.3087
120,9861 4056
0,6502 / 0,8035
X/X
Word2vec
0.6525 / 0.6267
89.5472; 122.3465
0.6415 / 0.8139
X/ X
Node2vec
0.4387 0.2938
91.5226 / 124.4122
0.6809 0081
X/X X
Transformer
0.4305 / 0.3645
91.3093 1358
0.6662 / 0.8426
X/X
BERT
0.6780 / 0,6251
90.2442 123.2867
0.6898 / 0.851
X/X
Toast
0.6251 / 0.6182
88.0744 116. 7965
0.6743 0.7362
X/X
ICLRNT
0.7445 / 0.7
92.3900 125.5088
0.5504 0.7442
X/X
START
0.4413 / 0.3575
118.0605 162.0801
0.6778 0.8072
X/X
JGRM
0.7745 / 0.7622
87.87087
119.99213
0.7742 0.9063
X X
MVTraj
0.8290 / 0.8159
54.9044f 85.38474
0.69045 / 0.855t
0.66300 / 0.8144
SMARTraf2
0.8407 0.8298
35.0689 60.9156
0.7409f 0.9069
0.6675 / 0.8392
Bold denotes the best result, and $ denotes the second and third best result.
4.2 Performance Comparison
Table 1 and Table 4 compare the performance of SMARTraj" against various baseline methods on the Chengdu and Xi'an an datasets across multiple trajectory representation tasks.
SMARTraj² consistently outperforms ofate-oftthe-att baselines across evaluated tasks. Specifically, in Chengdu, SMARTraj² reduces MAE by 29.30% and RMSE by 23.75% in travel time estimation, compared MV'Tra.. A similar improvement is observed in Xi' Xi'an, highlighting the model's ability to generalize across cities with distinct mobility patterns.
Furthermore, unlike basclines that train independently data from single city per experiment, SMARTraj" is trained across multiple cities simultancously, overcoming the structural heterogeneity that limits baseline methods. This enables SMARTraj² to remain stable in new urban environments without requiring retraining from scratch. Additionally, the personalized gating mechanism dynami- cally adjusts feature contributions across cities, alleviating the seesaw phenomenon, and ensuring consistent and stable performance across diverse urban settings.
4.3 Model Analysis
We assess the impact of pre-training on model performance across two datasets: Chengdu (Fig. and Xi'an an (Fig. Specifically, we evaluate two distinct training paradigms:
Pre-train: This corresponds to the original SMARTraj's where self- eeffsuuprviied objectives are first used to pre-train the trajectory encoder. The model is then fine-tuned on either the travel time estimation task or the destination road prediction task.
No Pre-train: This variant is trained in an end-to-end manner, where both the trajectory encoder and the prediction head randomly initialized and jointly optimized from scratch using supervised task-specific labels.
We observe that our results consistently demonstrate that pre-training significantly improves model effectiveness compared to training from scratch. We observe that pre-training not only accelerates convergence but also reduces dependency labeled data during fine-tuning, making the model more robust to data scarcity.
4.4
Ablation Study
We conduct comprehensive ablation study to evaluate the contribution of key components in our method. Specifically, we examine the following model variants:
w/o diff loss: Removes the difference loss Cairs. which applies soft orthogonality constraints to disentangle domain-invarian and domain-specific features.
https://outreach http://///ooeeeaccc.icccccscieee https://www www.swwwenttteeo www.nsteeeea.....m org/

---

Travel Time Estimation
Destination Road Prediction
0.80
1.1
140
180
50 train
pra-train
1.0
120
160
0.70
100
140
0.65
MAE
120
0.60
0.8
80
100
0.55
0.7
60
80
0.50
40
0.45
0.40
0.5
10
20
30
40
50
10
20
30
40
50
Training Data Size
Training Data Size
(a) Travel Time Estimation
(b) Destination Road Prediction
Figure 3: Effect of Pre-training in Chengdu.
Table 2: Ablation Study in Xi' an.
Road Label
Travel Time
Destination Road
Destination Grid Acc@ Acc@
Method
Micro-F1 Macro-F1
MAE/ RMSE
Acc@ Acc@5
SMARTraj2
0.8407 / 0.8298
35.0689 60.9156
0.7409 0.9069
0.6675 0.8392
diff loss
0,8500 0.8366
44.8105 73.8862
0,6025 0.8125
0.4866 0.7119
w/o gating
0.8387 0.8279
40.3469 68. 68.62116
0.6787 0 0.8478
0.5244 0,7200 X X
w/o grid
0.8233 0.8186
72.6226 105.6123
0.6604 0.8402
w/o GPS
0.7987 / 0, 7833
73.2965 106.
0,5446 0.7667
0,4110 0..6351
w/o route w/o inperict
X / X
74.5902 106.7897
0,5924 0.8049
0.4311 / 0.6633
0.7415 / 0, 7,728
56,5380 71.517
0,4770 0,7001
0,2556 / 0,5315
w/o gpting+
0.7637 / 0.7574
54.8681 / 84.8157
0.6300 / 0.8294
0.5466 / 0.7636
w/o gating: Excludes the personalized gating mechanism, which injects city-level and trajectory-level specific information, enabling adaptive feature modulation.
As shown in Table and Ta both components contribute significantly to model performance. Removing the difference loss (w/o diff loss) results severe performance degradation, demonstrating that Lais effectively separates domaanninvvrriant invariaan and domain-specifii information, enabling stable spatio-temporal modeling across cities. Excluding the personalized gating mechanism (w/o gating) also leads to notable performance drops, suggesting that gating plays a key role in dynamically balancing the contributions of city-specific and global features.
and
Moreover, we conduct an ablation study to evaluate the model under limited-viers settings. The results below demonstrate that our method maintains reasonable performance even when only GPS data is available.
4.5 Parameter Sensitivity
We perform a sensitivity analysis on key hyperparameters: the scaling factor (defined in Eq 8 and the weight ratio between wi and W2 (defined in Eq(15). where wi and we are the weights for the difference loss and masked language modeling loss, respectively. Results for travel time estimation in Xi' an are presented in Fig. Due to space limitations, additional results for destination road prediction are provided in Appendix B.2.4] with consistent trends.
Fig. 4(a) illustrates that y achieves optimal performance when set to 2. This factor controls the output range of gate scores, which modulate the embeddings for different cities and trajectories. Specifically, 7 = effectively balances the modulation by restricting the gate values to the range [0, 2] and centering them around 1. Fig. ( (efo)sstttssttss that the best performance is attained when the weight ratio wi w' = Ratios wi 12 < weaken the orthogonality constraint, while ratios
w1 w2 1 disrupt the balance between loss components, both leading to performance degradation.

---

Travel Time Estimation
Travel Time Estimation
75.0
90
47.5
72.5
80-
-100
42.5
67.5
70
-90
RMS
60-
37.5
62.5
50-
-70
32.5
57.5
60
30
55.0
0.5
1:3
1/2
2.1
3.1
gamma
w2:w2
(a)
(b) WT
W2
Figure Parameter Sensitivity Analysis on Travel Time Estimation Xi an.
5
Conclusion
In this paper, we propose SMARTraj² novel method for multi-view spatio-temporal trajectory representation learning that addresses the critical limitation of generalization across diverse urban scenes. We identified two key challenges that hinder the performance of existing approaches: multi-city structural heterogeneity, where cities exhibit significant differences in spatio-temporal patterns, and the amplified seesaw phenomenon, which arises when balancing performance across multiple cities, views, and tasks. To overcome these challenges, SMARTraj² leverages feature disentanglement module to separate domain-invariint and domain-specific features, enabling the model capture generalized spatio-temports patterns while preserving city-specific characteristics. Additionally, personalized gating mechanism dynamically adjusts the contributions of these features, mitigating the seesaw effect and ensuring stable performance across diverse urban scenes. Extensive experiments on real-world datasets show that SMARTraj" consistently outperforms state-of-the-- methods, demonstrating its ability to generalize effectively across cities with distinct mobility patterns, and proving its robustness in real-world applications.
Limitation and Future Work. Although the proposed framework demonstrates strong adaptability and performance across two representative cities (Chengdu and Xi'an), several limitations remain. The model depends multi-view urban data, and its performance may be influenced by missing modalities or inconsistent data quality across cities. Future work will focus on developing robust data fusion and modality-adapttiv mechanisms, extending experiments to larger city networks (e.g.. 10+ citics) to validate scalability, and exploring efficient training and inference strategies such as parameter sharing, model compression, and distributed learning.
Social Impact. The proposed framework has potential societal benefits in improving urban mobility management, traffic forecasting, and resource allocation. However, it also raises important ethical and privacy concerns, particularly when dealing with trajectory location-based data. Individual mobility traces may reveal sensitive information about users' habits or identities, posing privacy risks if mishandled. To mitigate these risks, striet data anonymization, aggregation, and de-identification procedures should be enforced before model training. Additionally, data access should comply with local data protection regulations and institutional review protocols.
Acknowledgments and Disclosure of Funding
This work supported by NSFC No. 62502499, NSFC No. 62372430, NSFC No. 62502505, the Youth Innovation Promotion Association CAS No. 12, the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20241758 the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20251078, and the China Postdoctoral Science Foundation 15.2025
10

---

References
[1] A. Ashukha, A. Lyzhov, D. Molchanov, and Vetrov. Pitfalls of in-domain uncertainty estimation and ensembling in deep learning. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, 2020.
[2] Y. Bai, X. Geng, K. Mangalam, A. Bar, A. L. Yuille, T. Darrell, J. Malik, and A. A. Efros. Sequential modeling enables scalable learning for large vision models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, June 16-22, 2024, pages 22861-22872. IEFE, 2024.
[3] K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan. Domain separation networks. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 343-351, 2016.
[4] T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. MerrerrtVosss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei. Lan- guage models are few-shot learners. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurlPS 2020, December 6-12, 2020, virtual, 2020.
[5] Bucci, A. D'Innocente, Liao, F. M. Carlucci, B. Caputo, and T. Tommasi. Self-supervised learning across domains. IEEE Trans. Pattern Anal. Mach. Intell., 44(9):5516-5522 2022.
[6] M. Chen, Y. Zhao, Y. Liu, X. Yu, and K. Zheng. Modeling spatial trajectories with attribute representation learning. IEEE Trans. Knowl. Data Eng., 94(4):1902-199) 2022.
[7] Y. Chen, X. Li, G. Cong, Z. Bao, C. Long, Y. Liu, A. K. Chandran, and R. Ellison. Robust road network representation learning: When traffic patterns meet traveling semanties. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pages 211-220, 2021.
[8] Y. Chen, W. Huang, K. Zhao, Y. Jiang, and G. Cong. Self-super representation learning for geospatial objects: A survey. Inf. Fusion, 123:103265, 2025.
[9] Y. Chen, X. Li, G. Cong, Bao, and C. Long. Semantic-enaanced representation learning for road networks with temporal dynamics. IEEE Trans. Mob. Comput., 24(10):9413-9427 2025.
[10] Devlin, M. Chang, K. Lee, and K. Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguisties: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pages 4171-4188 1866. Association for Computational Linguistics, 2019.
[11] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby. An image is worth 16x 16 words: Transformers for image recognition at scale. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021.
[12] N. Dryden and T. Hoefler. Spatial mixture-of-experts. In Advances in Neural Information Processing Systems 35: Annual Conference Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28- December 9, 2022, 2022.
[13] X. Fang, J. Huang, F. Wang, L.. Zeng, H. Liang, and H. Wang. Constgat: Contextual spatial- temporal graph attention network for travel time estimation at baidu maps. In KDD) '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23-27, 2020, pages 2697-2705. ACM, 2020.
[14) X. Fang, J. Huang, F. Wang, L. Liu, Y. Sun, and H. Wang. SSML: self-supervised meta-learner for en route travel time estimation at baidu maps. In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14- 18, 2021, pages
ACM, 2021.
11

---

[15] A. Grover and J. Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. pages 855-864 2016.
[16] S. Hu, K. Zhang, Z. Chen, and L. Chan. Domain generalization via multidomain discriminant analysis. In Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence. UAI 2019, Tel Aviv, Israel, July 22-25, 2019. volume 115 of Proceedings of Machine Learning Research, pages 292-302. AUAT Press, 2019.
[17] T. Ishida, I. Yamane, T. Sakai, G. Niu, and M. Sugiyama. Do we need zero training loss after achieving zero training error? In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pages 4604 4614. PMLR, 2020.
[18] J. Ji, J. Wang, J. Wu, B. Han, J. Zhang, and Y. Zheng. Precision cityshield against hazardous chemicals threats via location mining and sulf-supervised learning. In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14- 18, 2022, pages 3072-3080. ACM, 2022.
[19] Jiang, D. Pan, H. Ren, X. Jiang, C. Li, and J. Wang. Self-supervised trajectory representation learning with temporal regularities and travel semantics. In 2023 IEEE 39th international conference on data engineering (ICDE), pages 843-855. IEEE, 2023.
[20] L. Jiang, C. Chen, and C. Chen. L2MM: learning to map matching with deep models for low-quality GPS trajectory data. ACM Trans. Knowl. Discov. Data, 17(3):99: 2023,
[21] Y. Liang, K. Ouyang, Y. Wang, X. Liu, H. Chen, J. Zhang, Y. Zheng, and R. Zimmermann. Trajformer: Efficient trajectory classification with transformers. In Proceedings of the 31 ACM International Conference on Information & Knowledge Management, pages 1229-1237, 2022.
[22] Y. Lin, H. Wan, S. Guo, J. Hu, C. S. Jensen, and Y. Lin. Pre-training general trajectory embeddings with maximum multi-view entropy coding. IEEE Trunsactions on Knowledge and Data Engineering, 2023.
[23] F. Liu, D. Wang, and Z. Xu. Privacy-preserving travel time prediction with uncertainty using GPS trace data. IEEE Trans. Mob. Comput., 2211):417-428 2023.
[24] Z. Ma, Z. Tu, X. Chen, Y. Zhang, D. Xia, G. Zhou, Y. Chen, Y. Zheng, and J. Gong. More than routing: Joint GPS and route modeling for refine trajectory representation learning. In Proceedings of the ACM on Web Conference 2024, WWW 2024, Singapore, May 13-17, 2024, pages 3064-301 ACM, 2024.
[25] Z. Mao, Z. Li, D. Li, L. Bai, and R. Zhao. Jointly contrastive representation learning on road network and trajectory. In Proceedings of the 31st ACM International Conference Information & Knowledge Management, pages 1501-1510, 2022.
[26] T. Mikolov. Efficient estimation of word representations in vector space. arXiv preprint 1rXiv:1301.378% 2013.
[27] T. Nguyen, J. Brandstetter, A. Kapoor, K. Gupta, and A. Grover. Climax: A foundation model for weather and climate. In International Conference on Machine Learning, ICMI, 2023, 23-29 July 2023, Honolulu, Hawaii, USA, volume 202 of Proceedings of Machine Learning Research, pages 25904-25933 -29938.. PMLR, 2023.
[28] C. Park, T. Kim, J. Hong, M. Choi, and J. Choo. Pre-training contextual location embeddings in personal trajectories via efficient hierarchical location representations. In Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part VII, volume 14175 of Lecture Notes in Computer Science, pages 125- Springer, 2023.
[29] B. Prenkaj and P. Velardi. Unsupervised detection of behavioural drifts with dynamic clustering and trajectory analysis. IEEE Trans. Knowl. Data Eng., 36(5):2257-2270, 2024.
12

---

[30] T. Qian, F. Wang, Y. Xu, Y. Jiang, T. Sun, and Y. Yu. CABIN: A novel cooperative attention based location prediction network using internaleeterrnl trajectory dependencies. In Artificial Neural Networks and Machine Learning- ICANN 2020 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part II, volume 12397 of Lecture Notes in Computer Science, pages 221-532. Springer, 2020.
[31] T. Qian, Y. Xu. Z. Zhang, and Wang. Trajectory prediction from hierarchical perspective. In MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10 14, 2022. pages 6822-6830. ACM, 2022.
[32] T. Qian, Y. Chen, G. Cong, Y. Xu, and F. Wang. Adaptraj: A multi-source domain generalization framework for multi-agent trajectory prediction. In 40th IEEK International Conference on Data Engineering, ICDE 2024, Utrecht, The Netherlands, May 13-16, 2024, pages 5048-5060. IEEE, 2024.
[33] T. Qian, J. Li, Y. Chen, G. Cong, T. Sun, F. Wang, and Y. Xu. Context-enhanced multi-view trajectory representation learning: Bridging the gap through self-supervised models. CoRR, abs/2410.13196 2024.
[34] T. Qian, Y. Wang, Y. Xu, 7. Zhang, I. Wu, Q. Qiu, and R. Wang. A model-agnostic hierarchical framework towards trajectory prediction. J. Comput. Sei. Technol. 40(2):322-339, 2025.
[35] W. Qin, J. Tang, and S. Lao. Deepfr: A trajectory prediction model based on deep feature representation. Inf. Sci., 604:226- 248, 2022.
[36] R. Rombach, A. Blattmann, D. Lorenz, Esser, and B. Ommer. High-resolutio image synthesis with latent diffusion models. In IEEE/CVF Conference Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, pages 10674- 10685. IEEE, 2022.
[37] T. Szymanski and M. Lemmon. Adaptive mixtures of local experts are source coding solutions. In Proceedings of International Conference on Neural Networks (ICNN'88) San Francisco, CA, USA, March 28- April I, 1993, pages 1391-1396. IEEE, 1993.
[38] H. Touvron, L. Martin, K. Stone. P. Albert, A. Almahairi. Y. Babaei, N. Bushlykov, S. Ba- tra, P. Bhargava, S. Bhosale, D. Bikel, L. Blecher, C. Canton-Ferrer, M. Chen, G. Cucurull, D. Esiobu, J. Fernandes, J. Fu, W. Fu, B. Fuller, C. Gao. V. Goswami, N. Goyal, A. Hartshorn, Hosseini, R. Hou, H. Inan, M. Kardas, Kerkez, M. Khabsa, Kloumann, A. Korenev, Koura, M. Lachaux, T. Lavril, J. Lee, D. Liskovich, Y. Lu, Y. Mao, X. Martinet, T. Mihaylov, Mishra, Molybog, Y. Nie, Poulton, J. Reizenstein, R. Rungta, K. Saladi, A. Schelten, R. Silva, E. M. Smith, R. Subramanian, X. E. Tan, B. Tang, R. Taylor, A. Williams, J. X. Kuan, Xu, Z. Yan, I. Zarov, Y. Zhang, A. Fan, M. Kambadur, Narang, A. Rodriguez, R. Stojnic, S. Edunov, and T. Scialom. Llama 2: Open foundation and fine-tuned chat models. CoRR, abs/2307.09288, 2023.
[39] A. Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017.
[40] H. Wan, Y. Lin, S. Guo, and Y. Lin. Pre-training time-aware location embeddings from spatial-temporal trajectories. IEEE Trans. Knowl. Data Eng., 34(11):5510-5523 2022.
[41] C. Wang, L. Chen, S. Shang, C. S. Jensen, and P. Kalnis. Multi-scale detection of anomalous spatio-temporal trajectories in evolving trajectory datasets. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024, Barcelona, Spain, August 25-29, 2024, pages 2980-2990. ACM, 2024.
[42] C. Wang, S. M. Erfani, T. Alpean, and C. Leckie. Decortad: Diffusion based conditional representation learning for online trajectory anomaly detection. In ECAI 2024 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024), volume 392 of Frontiers in Artificial Intelligence and Applications, pages 2757-2764. IOS Press, 2024.
13

---

[43] C. Wang, J. Huang, Y. Wang. Z. Lin, X. Jin, X. Jin, D. Weng, and Y. Wu. A deep spatiotemporal trajectory representation learning framework for clustering. IEEE Trans. Intell. Transp. Syst. 25(7):7687-7700, 2024.
[44] H. Wang, S. Zeng. Y. Li, and D. Jin. Predictability and prediction of human mobility based on application-collecta location data. IEEE Trans. Mob. Comput., 20(7):2457-2444 2021.
[45] J. Wang, C. Lan, C. Liu, Y. Ouyang. T. Qin, W. Lu, Y. Chen, W. Zeng. and P. Yu. Generalizing to unseen domains: A survey on domain generalization. IEEE Trans. Knowl. Data Eng., 35(8):
8052-8072, 2023.
[46] D. Wu, Z. Fang, Q. Sun, L. Chen, H. Hu, F. Wang, and Y. Gao. Trajrecovery: An efficient vehicle trajectory recovery framework based on urban-scale traflic camera records. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024, Barcelona, Spain, August 25-29, 2024, pages 1979-5990 ACM, 2024.
[47] L.. Wu, Wang, N. Li, Sun, Qian, Y. Jiang. Wang. and Y. Xu. Modeling the covid- 19 outbreak in china through multi-source information fusion. The Innovation, 1(2), 2020.
[48] Y. Wu, L. Wang, S. Zhou, J. Duan, G. Hua, and W. Tang. Multi-stream representation learning for pedestrian trajectory prediction. pages 2875-2882. AAAI Press, 2023.
[49] C. Yang and G. Gidofalvi. Fast map matching, an algorithm integrating hidden markov model with precomputation. International Journal of Geographical Information Science, 32(3):547- 570, 2018
[50] S. B. Yang, C. Guo, J. Hu, J. Tang, and B. Yang. Unsupervised path representation learning with curriculum negative sampling. arXiv preprint arXiv:2106. 09373, 2021.
[51] S. B. Yang. Hu, C. Guo, B. Yang, and C. S. Jensen. Lightpath: Lightweight and scalable path representation learning. In Proceedings of the 29th ACM SIGKDD Conference Knowledge Discovery and Data Mining, pages 2999-3010, 2023.
[52] Y. Yu, H. Tang, F. Wang, L. Wu, T. Qian, T. Sun, and Y. Xu. TULSN: siamese network for trajectory-user linking. In 2020 International Joint Conference Neural Networks, IJCNN 2020, Glasgow, United Kingdom, July 19-24, 2020, pages 1-8. IEEE, 2020.
[53] Y. Yuan, J. Ding, J. Feng, D. Jin, and Y. Li. Unist: A prompt-cmpowered universal model for urban spatio-temporal prediction. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024, Barcelona, Spain, August 25-29, 2024, pages 4095 4106. ACM, 2024.
[54] Zhong, 7. Chi, L. Gu, Wang. Yu, and J. Tang. Meta-dmoe: Adapting to domain shift by meta-distillation from mixture-of-expert In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 December 9, 2022, 2022.
[55] K. Zhou, Z. Liu, Y. Qiao, T. Xiang, and C. C. Loy. Domain generalization: A survey. IEEE Trans. Pattern Anal. Mach. Intell., 45(4):43964441, 2023.
[56] Y. Zhu, J. J. Yu, X. Zhao, X. Wei, and Y. Liang. Unitraj: Learning a universal trajectory foundation model from billion-scale worldwide traces. CoRR, abs/2411.00 1.03859, 2024.
14

---

A Related Work
In this section, we first review existing research on trajectory representation learning, followed by an exploration of transfer learning approaches in spatio-temporal domains.
A.1 Spatio-Temporal Trajectory Representation Learning
Spatio-temporal trajectory representation learning has garnered substantial attention due to its rel- evance in various applications, including intelligent transportation systems [31 28 urban plan-
ning [4 44]. and environmental monitoring [8 44 Existing approaches can be categorized into
task-specific and task-agnostic methods.
Task-specific methods are designed to optimize performance on a particular downstream task, such anomaly detection 42 41, clustering [43 29 and trajectory forecasting [3 B5]. These methods typically optimize trajectory encoders with task-specific objectives, resulting in high performance for the targeted application. However, their narrow focus limits their ability generalize across diverse tasks and makes them less efficient real-world multi-tase scenarios [7]
Task-agnostic methods, the other hand, aim to learn generalized representations that can be applied across various downstream tasks. Many of these methods employ seff-supervised learning techniques [22 & to enable flexible generalization. These methods are further divided into single- view and multi-view approaches. Single-view methods rely on a single spatial aspect of trajectory data, such raw GPS coordinates [21 [56), road network routes [50] [SI] or POI sequences [40] 66. While these methods effectively capture patterns within the chosen view, they often fail to model the full complexity of -eatioltemporal data, partieularly in diverse urban scenes. Multi-view methods B3 24 attempt to address this limitation by integrating multiple data sources, offering a richer and more comprchensive understanding of movement patterns.
Despite these advancements, existing multi-view approaches are constrained datasets from single city, significantly limiting their generalization capability to other urban scenes with distinct characteristics. Our proposed SMARTraj² method addresses this critical gap by focusing on general- ization across cities. We disentangle Nomain-invarian and domain-specific representations, ensuring effective generalization while preserving city-speciff characteristics. Additionally, a personalized gating mechanism is operated at both city-level and trajectory-level to dynamically stabilize the contributions of different views and tasks, mitigating the amplified seesaw phenomenon.
A.2 Transfer Learning for Spatio-Temporal Trajectories
The concept of transfer learning has seen tremendous success in fields such as natural language processing 44] TO, 38 and computer vision [T] 36 2]. In the context spatio-temporal trajectory analysis, transfer learning has recently emerged as a promising direction to handle the complexities inherent in this domain. These challenges include irregular sampling intervals, spatial hetcrogeneity, and intricate temporal dependencies that require sophisticated models to generalize effectively across diverse datasets.
Several studies have explored the application of transfer learning to spatio-temporal trajectory data. For example, (56) maintains robust representation capabilities for GPS data with varying qualities. effectively handling issues like noise, missing values, and inconsistent sampling rates. [53] partitions cities into non-overlapping areas and trains across multiple cities to achieve universal spatio-temporal
prediction, excelling in few-shot and zero-shot tasks. [27] presents generalizable deep learning model for weather and climate science that can handle heterogeneous datasets across different spatio-temporal dimensions.
However, existing transfer learning methods predominantly focus on single-view trajectory data (e.g.. GPS or grid data), making them less effective for more complex scenarios involving multi-view data. Our work addresses this gap by introducing solution that operates multi-city, multi- view, and multi-task settings. The incorporation of multiple views exacerbates the challenges of transfer learning, particularly with respect to the seesaw phenomenon, where performance trade-off's between different views and tasks become more pronounced. Our method mitigates this issue by dynamically adjusting the contribution of shared and domain-specific features using personalized gating mechanism, thereby stabilizing performance across cities, views, and tasks.
15

---

B Technical Appendices and Supplementary Material
B.1 Experimental Setting
B.1.1 Details of Datasets
To facilitate clearer comparison between the two datasets, present key statistics in Table These statistics help highlight the differences in composition between the Chengdu and Xi'an an datasets, ensuring comprehensive understanding of their characteristics.
For comparative purposes, our preprocessing steps are aligned with those used in prior studies 244 25] Specifically, obtain route view trajectories 7", apply map-matching algorithm 49 to convert raw GPS data into sequences road segments, thereby producing road-network-constrriinn trajectories in the route view. Additionally, ensure the relevance of the data, preprocess the road network by filterigg out segments that are not traversed by any trajectory. To obtain grid view trajectories 7 utilize Points of Interest (POI) data, collected from external soured to enhance the semantic information of grid cells. Rach grid cell's semantic representation is normalized based on the POIs it contains.
We further filter the trajectories to ensure data quality and consistency across experiments. Trajectories must contain between 10 and 100 road segments, 10 and 100 grid cells, or 10 and 256 GPS points. Any trajectories that do not meet these criteria are excluded from the dataset.
Each dataset consists of 13 distinct categories of points-of-interes (POI). representing a diverse range of urban functions. These categories include Dining, Scenery, Public Facilities, Shopping. Transportation, Education, Finance, Residential. Life Services, Sports, Healthcare, Government Offices, and Accommodation Services. These categories are crucial for enriching the semantic features of grid cells and offer comprehensive representation of the urban scene.
Table 3: Datails of the Datasets
Datasets
Chengdu
Xi'an
Time Interval
3.07
3.10
Number of Nodes
6450
4996
Avg. Node Degree
5.08
4.75
Number of Edges
16398
11864
Avg. GPS Trajectory Length (m)
2829.16
2797.26
Avg. Route Trajectory Length
15.26
15.96
Avg. Road Travel Speed (m/s) 8))
6.91
6.22
Avg. Trajectory Travel Time (s)
426.31
467.47
B.1.2 Compared Methods
We compare SMARTraj2 against several baseline methods that employ self-supervised training approaches and are designed for general-purposss trajectory representation learning, suitable for multiple downstream tasks. These baselines offer solid foundation for assessing the advantages of our approach under consistent experimental conditions.
Random: it initializes trajectory representations randomly, providing a reference for under- standing the performance improvements achieved by more sophisticated models.
Word2Vec 26 :
learss representations using the skip-gram model, which captures semantic similarities between road segments by treating them as words in a sequence, based on co- occurrence statistics within trajectories.
Node2Vec [15]
learns node representations in graph via biased random walks, which explore node neighborhoods to capture both local and global structural properties.
Transformer 39 employs self-attention mechanism to model complex dependencies in sequential data.
http://geodata, pku..apppkuua edu.
16

---

BERT [0: it is pre-trained to learn deep bidirectional representations by conditioning on both left and right contexts all layers.
Toast 77: it first pre-trains node embeddings using Node2Vec, then fine-tunes the represen- tations with Transformer, incorporating auxiliary traffic context information.
JCRLNT 25 it employs separate graph and trajectory encoders, training the model on three comparative tasks to refine the quality of the learned representations.
START [99] introduces trajectory encoder that incorporates travel semantics and tempo- ral continuity, trained with two self-supervised tasks to improve trajectory representation quality.
JGRM [24]:
it combines GPS trace data with route traces to model road network constraints, capturing both spatial and temporal dynamics.
33 it captures multiple structural and semantic aspects of trajectory data from three different spatial views, offering rich and diverse representation suited for various downstream tasks.
MVTraj
None the bascline methods effectively tackle the challenge of multi-city structural heterogeneity, critical factor for generalization across urban scenes. Consequently, these methods fail to leverage datasets that encompass multiple cities, and are instead trained on data from a single city per experiment.
B.1.3 Detail of Evaluation Metries
We employ range evaluation metrics to comprehensively compare the performance of different methods across various tasks. These metrics are designed to capture different aspects of prediction accuracy, from classification performance regression error.
This metric aggregates the contributions of all classes into a single overall Fl value, providing balance between precision and recall across the entire dataset. It is calculated by summing the true positives (TP), false positives (FP), and false negatives (FN) across all classes to derive the overall precision and recall.
Micro-Fl:
Micro F1 = 2 x Precisionall X Recallalt
Precisionals + Recallat
This metric useful when the dataset contains large class imbalance, it treats all instances equally.
Macro-F1: Unlike Micro-F1, Macro-F1 treats each class equally by calculating the Fl score for each class independently and then averaging these scores:
Macro F1 N Σ F1,
where Fl, is the Fl score for the i-th class, and N is the total number of classes. Macro-Fl is particularly effective when dealing with imbalanced datasets, as it ensures that all classes are equally represented in the final metric.
Mean Absolute Error (MAE): MAE quantifies the average magnitude of the errors in a set of predictions, providing straightforward interpretation of the average error magnitude:
MAE
y
Qsl
where is the true value, 0t is the predicted value, and n is the total number of observations. MAE is sensitive to small deviations and provides a clear measure of average prediction accuracy.
Root Mean Squared Error (RMSE): RMSE measures the square root of the average squared differences between predicted and true values, emphasizing larger errors due to the squaring differences:
RMSE
(Us
01)
71
17

---

This metric is useful for capturing the variance in prediction errors, with larger errors being penalized more than smaller ones.
Accuracy k (Acc@k): This metric evaluates whether the true label appears in the top-k predicted labels for each instance:
Acc@k Σ My E Ui )
where Y4 is the true label, 9; represents the set of top-k predicted labels, and I(-) is an indicator function returning 1 if the true label y/i within the top-k predictions, and otherwise. Accol is particularly relevant in ranking and recommendation tasks, offering insight into the effectiveness of the model in providing accurate top-k suggestions.
B.1.4 Implementation Details
Our evaluation follows a two-stage process to ensure robustness and fairness. In the first stage, the encoder is pre-trained on large of unlabeled trajectory data (e.g., 50K trajectories from the Chengdu dataset) to learn informative trajectory representations. In the second stage, smaller labeled subset (e.g., 12K labeled trajectories from the Chengdu dataset) is used to fine-tune the model and train task-specific models for classification or regression. These task-specific models predict outputs such as road labels, travel time, destination road segment IDs, or destination grid indices.
To optimize the model, we use the AdamW optimizer for both pre-training and fine-tuning. The training process spans 70 epochs with batch size of 64. The initial learning rate is set to 0.0001, and we adopt warm-up policy that linearly increases the learning rate during the first five epochs. Afterward, cosine annealing schedule is employed gradually reduce the learning rate in the subsequent epochs.
To enhance the model's robustness, we introduce masking mechanism during training. Specifically, we apply a masking length of 2 with a probability of 20%.
B.2
Additional Experiments
B.2.1
Additional Performance Comparison
Table 4: Overall Performance in Chengdu
Road Label
Travel Time MAE/ / RMSE
Destination Road
Destination Grid
Method
Micro-F1 / Macro-F1
Acc@ I Acc@5
Acc@ 1 Acc@5
Random
0.4363 0.3152
112.3310/ 141.6182
0.651 / 0.7795
XIX
Word2vec
0.5857 0.5767
85.4754 / 113.8926
0.6093 0.7717
XIX
Node2vec
0.5535 0.5306
85.9276 / 114.4905
0.604 / 0.7611
XIX
Transformer
0.3753 0.3460
88.3027 117.2306
0.6297 0.7969
X/X
BERT
0,5516 / 0.5363
8688267 / 115,4555 45.32
0.5994 0.7755
X/X
Toast
0.7145 / 0.6755
92.2311 / 125.6123
0.5966 / 0.773
X/X
JCLRNT
0,6100 0.6037
90.9430 / 116.6238
0.5147 / 0.7953
X/X
START
0.3526 0.1869
112.0348 148.3855
0.6872/ 0.7764
X/X
JGRM
0.71983 0.7228
82.8468; / 110,3405t
0.73041 0.8731
X X
MVTraj
0.7206 / 0.7326
48.5581 71.8248f
0.70211 0,8597
0.79271 0,9105
SMARTraj
0.7461 0.7473
34.3311 54.7641
0.7395 0.881
0.8082 0.9289
& Bold denotes the best result, f and denotes the second and third best result.
Tab. presents the ablation study results on the Chengdu dataset, which are consistent with those obtained in Xi an. The results demonstrate that each proposed component contributes significantly to overall model performance. Moreover, the Chengdu results further validate the robustness and stability of SMARTraj3 under varying urban conditions and data distributions.
B.2.2 Additional Model Analysis
Fig. presents the model analysis results on the an dataset, which are consistent with those observed in Chengdu. The results confirm that pre-training substantially enhances model performance.
18

---

Travel Time Estimation
Destination Road Prediction
0.80
1.1
140
180
50 train
pre-trainn
1.0
120
160
0.70
100
140
0.65
MAE
80
120
R
0.60
0.8
100
0.55
0.7
80
0.50
40
0.45
0.40
0.5
10
20
30
40
50
10
20
30
40
50
Training Data Size
Training Data Size
(a) Travel Time Estimation
(b) Destination Road Prediction
Figure 5: Effect Pre-training in Xi'an.
Compared to training from scratch, the pre-trained model exhibits faster convergence and lower prediction errors, highlighting the effectiveness of self-supervised trajectory representation learning. Moreover, the performance gain remains stable when the amount of labeled data is reduced, demonstrating the model's robustness and data efficiency in low-supervision scenarios.
B.2.3 Additional Ablation Study
Table 5: Ablation Study in Chengdu.
Road Label
Travel Time MAE/ / RMSE
Destination Road
Destination Grid Acc@ 1/ Acc @5
Method
Micro-F1 Macro-F1
Acc@ 1 Acc@5
SMARTraj2 w/o diff loss
0.7461 /0.7473
34.3311 54.7641
0.7395 0.8810 0.6006 0.7817
0.8082 0.9289
0.7320 0.7314
35.0011 /57,7368
0.6777 0.8558
w/o gating
0.7378 / 0.7399
34,4141 55.7714
0.6037 0.7868
0.6796 0.8583
Tab. 5 presents the ablation study results on the Chengdu dataset, which are consistent with those obtained in Xi' an. The results clearly demonstrate that both the difference loss (Caif1) and the personalized gating mechanism play essential roles in the model's performance. These findings collectively validate the effectiveness and complementarity of both modules in enhancing the model's generalization capability.
We have also conducted additional ablation experiments where the model is trained using only data from single city, and compared it with the multi-city training setting. The results for Xii an and Chengdu are presented in Tab.
Across all tasks and evaluation metrics, multi-city learning consistently outperforms single-city training. This demonstrates that incorporating data from multiple cities enables the model to learn more generalized and transferable patterns, leading to better performance even on individual city tasks. These findings validate the effectiveness and necessity of multi-city learning.
Table 6: Ablation Study.
Road Label
Travel Time MAE/ RMSH
Destination Road Acc@) 1 Acc@
Destination Grid Acc@) 1 Acc
City
Method
Micro-F1 / Macro-F1
multi-city single-city
0.8407 0.8298 0.8325 0.8144
35.0689 60.9156
0.7409 0.9069
0.6675 0.8392 0.6569 0.8133
Xi'an
47.7116 / 76.4029
0.6973 0.7915
multi-city
0.7461 0.7473
34.3311 / 54.7641
0.7395 0.8810
0.8082 0.9289
Chengdu
single-city
0.7243 / 0.7273
43.6200 / 65.1772
0.7011 0.8610
0.7887 0.8991
19

---

Destination on Road Prediction
Destination Road Prediction
0.50
0.84
0.80
0.000
0.78
0.92
0.70
0.875
0.65
0.850
0.99
0.60
0.825
0.86
0.58
0.800
0.70
0.84
0.775
0.88
0.750
0.66
0.80
2.1
1:3
3.1
2.1
1.2
1:3
gamma
w1:w2
(a)
(b) WT
122
Figure Parameter Sensitivity Analysis on Destination Road Prediction Xi'an an.
B.2.4
Additional Parameter Sensitivity
Fig. 6 presents the sensitivity analysis results for the destination road prediction task in Xi an, which are consistent with those obtained for travel time estimation. These consistent trends across tasks further demonstrate the stability and robustness of the proposed model with respect to hyperparameter choices.
B.2.5 Model Efficiency
Table Efficiency Comparison on Xi'an.
Model Size (MBytes)
Train Time (min/epoch)
Inference Time
(milliseconds)
Random
0.374
Word2Vec
8.0
0.2
0.404
Node2Vee Vec
7.6
0.2
0.328
Transformer
14
1.5
0.940
BERT
433
3.5
1.260
Toast
27
7.2
2.152
JCLRNT
13
1.3
0.792
START
94
17
4.700
JGRM
33
15
4.220
MVTraj
207
35
10.560
SMARTraj²
477
54
11.65
As shown in Tab. 7 while our method has larger model size compared to some baselines, its inference time remains within practical range. These results suggest that approach is feasible for real-world deployment.
20

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The main claims made in the abstract and introduction accurately reflect the paper's contributions and scope. Please see Abstract and Sec.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: The paper discusses the limitations of the work performed by the authors. Please see Sec. 4
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (c.g.. independence assumptions, noiseless settings, model sell-speciiiatiinn asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g. if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low or images are taken in low lighting. Or speech-to-tex, system might not be used reliably to provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and faimesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
21

---

Answer: [Yes]
Justification: The paper provides the full set of assumptions and a complete (and correct) proof. Please see Sec.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Question:
Answer: [Yes]
Justification: The paper fully disclose all the information needed reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper. The code and data are also provided. Please see Abstract and Sec. B.1.4
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how replicate the results, access a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how reproduce that algorithm. (b) If the contribution is primarily
model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should possible for other researchers to have some path to reproducing verifying the results.
22

---

5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, described in supplemental material?
Answer: [Yes]
Justification:
The paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results. Please see Abstract and Sec. 1.1
Guidelines:
The answer NA means that paper does not include experiments requiring code. Please see the NeurlPS code and data submission guidelines (https://nips.cm
for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https
Codeeubmii ssi criionPooiccy for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data,
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
The paper specify all the training and test details (c.g., data splits, hyperparam- eters, how they were chosen, type of optimizer, etc.) necessary to understand the results. Please see Sec. B.1.4.
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [Yes]
Justification: The paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments. Please see Sec. 4
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by emror bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
23

---

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables figures symmetric error bars that would yield results that are out range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
Question:
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
Answer: [Yes]
The paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed reproduce the experiments. Please see Abstract.
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics
Answer: [Yes]
Justification: The research conducted in the paper conform, every respect, does with the NeurlPS Code of Ethics. Please see Abstract.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g.. if there is special consid- cration due to laws or regulations their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: The paper discuss both potential positive societal impacts and negative societal impacts of the work performed. Please see Sec.

---

Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA No, they should explain why their work has societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious unintended uses (c.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., denloymemt of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms monitor how system learns from feedback over time, improving the efficiency and accessibility ML).
11. Safeguards
Question:
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Answer: [Yes]
Justification: The paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse. Please see Abstract.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
The creators or original owners of assets (e.g., code, data, models), used the paper, are properly credited and the license and terms of use explicitly mentioned and properly respected. Please see Abstract.
Justification:
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
25

---

The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (e.g.. CC-BY 4.0) should be included for each asset. For scraped data from a particular source (e.g.. website). the copyright and terms of service of that souree should be provided.
assets are released, the license, copyright information, and terms use in package should be provided. For popular datasets, paperssi thoode com com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information is not available online, the authors encouraged to reach out to the asset's ' creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification:
The paper does not release new assets.
Guidelines:
The answer NA means that the paper does release new assets.
Researchers should communicate the details the datasct/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According to the NeurlPS Code of Ethics, workers involved data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: The paper does not involve potential risks incurred by study participants.
Guidelines:
26

---

The answer NA means that the paper does not involve crowdsoureing nor research with
human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent)
may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: The LLM used only for writing, editing, and does not impact the core methodology, scientific rigorousness, or originality of the research.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to LLM policy dbtpeeee//eerrrsscc/////ffeerere for what should or should not be described.
27


Reviews:
[{"review_id": "QRr3Zw3jU5", "reviewer": "Reviewer_fyeL", "summary": "The paper introduces a novel method for multi-view spatio-temporal trajectory representation learning designed to address the challenges of generalizing across diverse urban scenes. The key contributions include: 1) Feature Disentanglement Module: Separates domain-invariant and domain-specific features using orthogonality constraints to handle multi-city structural heterogeneity. 2) Personalized Gating Mechanism: Dynamically adjusts contributions of different views and tasks to mitigate the amplified seesaw phenomenon in multi-city settings. 3) Experimental Validation: Demonstrates superior performance on benchmark datasets (Chengdu and Xi’an) across tasks like travel time estimation and destination prediction, outperforming state-of-the-art baselines.", "questions": "1) Does the computational cost of city-specific feature extractors become prohibitive as the number of cities increases?\n\n2) The method assumes aligned multi-view data (GPS, route, grid). How does it perform if one or more views are missing or noisy (e.g., sparse GPS signals)?\n\n3) Would the proposed method still work if only a single view (e.g., GPS-only) is available in some cities?\n\n4) The method is evaluated on two cities (Chengdu and Xi’an). Could it scale to larger city networks (e.g., 10+ cities)?", "limitations": "Suggestions for Improvement: Expand discussion on potential misuse (e.g., privacy risks from trajectory data) and safeguards (e.g., anonymization).", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "First to tackle multi-city, multi-view trajectory learning with explicit focus on generalization, addressing critical gaps in prior work (e.g., single-city limitations). And the feature disentanglement and gating mechanisms are well-designed, supported by ablation studies and theoretical grounding (e.g., orthogonality constraints). Additional discussion on the following aspects would enhance the paper: \n\n1) Does the computational cost of city-specific feature extractors become prohibitive as the number of cities increases?\n\n2) The method assumes aligned multi-view data (GPS, route, grid). How does it perform if one or more views are missing or noisy (e.g., sparse GPS signals)?\n\n3) Would the proposed method still work if only a single view (e.g., GPS-only) is available in some cities?\n\n4) The method is evaluated on two cities (Chengdu and Xi’an). Could it scale to larger city networks (e.g., 10+ cities)?", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer fyeL for the insightful comments. We address each point below: \n\n**W1\\&Q1**. We conducted additional experiments to evaluate computational cost and scalability. As shown in the table below, our method maintains a reasonable trade-off between model size and performance, even with the city-specific components. Details and further analysis will be included in the revised manuscript.\n| Method       | Model Size (MB) | Train Time (min/epoch) | Inference Time (ms) |\n|--------------|----------------:|-----------------------:|--------------------:|\n| Random       | -               | -                      | 0.374               |\n| Word2Vec     | 8.0             | 0.2                    | 0.404               |\n| Node2Vec     | 7.6             | 0.2                    | 0.328               |\n| Transformer  | 14              | 1.5                    | 0.940               |\n| BERT         | 433             | 3.5                    | 1.260               |\n| Toast        | 27              | 7.2                    | 2.152               |\n| JCLRNT       | 13              | 1.3                    | 0.792               |\n| START        | 94              | 17                     | 4.700               |\n| JGRM         | 33              | 15                     | 4.220               |\n| MVTraj       | 207             | 35                     | 10.560              |\n| Ours         | 477             | 54                     | 11.65               |\n***\n**W2\\&Q2**. We acknowledge that the route view is city-specific due to locally defined road segment IDs, which limits its transferability. However, other views (GPS, POI, grid) are transferable across cities when similar raw data is available. Our model is designed to function effectively using only the transferable views, and we will elaborate on this in the revised manuscript.\n***\n**W3\\&Q3**. We conducted an ablation study to evaluate the model under limited-view settings. The results below demonstrate that our method maintains reasonable performance even when only GPS data is available. \n| Method   | Road Label Classification |           | Travel Time Estimation |           | Destination Road Prediction |           | Destination Grid Prediction |           |\n|----------|---------------------------|-----------|------------------------|-----------|-----------------------------|-----------|-----------------------------|-----------|\n|          | Micro-F1 ↑                | Macro-F1 ↑| MAE ↓                  | RMSE ↓    | Acc@1 ↑                     | Acc@5 ↑   | Acc@1 ↑                     | Acc@5 ↑   |\n| Ours     | 0.8407                    | 0.8298    | 35.0689                | 60.9156   | 0.7409                      | 0.9069    | 0.6675                      | 0.8392    |\n| w/o grid | 0.8233                    | 0.8186    | 72.6226                | 105.6123  | 0.6604                      | 0.8402    | X                           | X         |\n| w/o GPS  | 0.7987                    | 0.7832    | 73.2965                | 106.1142  | 0.5446                      | 0.7667    | 0.4110                      | 0.6351    |\n| w/o route| X                         | X         | 74.5902                | 106.7897  | 0.5924                      | 0.8049    | 0.4311                      | 0.6636    |\n***\n**W4\\&Q4**. Although current experiments involve only two cities (Chengdu and Xi’an), our framework is designed to scale across multiple city domains. The dual-channel architecture (shared and city-specific) and personalized gating mechanism are inherently scalable without requiring architectural modifications. Future experiments on larger city networks will be explored and discussed in the revised manuscript.\n***\n**L1**. We will expand the manuscript to explicitly discuss ethical and societal considerations. Specifically: (1) A dedicated 'Limitations' subsection will address multi-view data dependency, non-transferable components, and potential domain shift issues. (2) The 'Societal Impact' section will be extended to discuss potential risks (e.g., privacy concerns from trajectory data), as well as safeguards such as data anonymization and aggregation."}, {"review_id": "mAT3LoBIBe", "reviewer": "Reviewer_ic1N", "summary": "This paper addresses an important problem in spatiotemporal learning—trajectory representation learning. The authors propose a novel model, SMARTraj^2, to learn trajectory embeddings from a multi-view and multi-city perspective. To align representations across different cities, the model incorporates a feature disentanglement module for learning domain-invariant and domain-specific features, and a personalized gating module for dynamically adjusting feature embeddings for different views. Experiments conducted on two real-world datasets, Chengdu and Xian, demonstrate the effectiveness of the proposed method.", "questions": "Q1.The authors should evaluate their model on more datasets, ideally with diverse spatial structures of cities, to validate the feasibility and generalizability of multi-city learning.\n\nQ2. The authors need to include a single-city setting in the ablation study to evaluate the contribution of multi-city learning.\n\nQ3. The authors need to provide efficiency analysis to prove that their model is superior in efficiency when the effectiveness improvement is not obvious.\n\nQ4.The proposed model is trained for 70 epochs, while most baselines are trained for only 30. The authors should justify this discrepancy and ensure a fair comparison.\n\nQ5. Trajectory similarity computation task is widely used in trajectory representation learning evaluation and appears in many baselines. The authors should not neglect this important downstream task.", "limitations": "yes", "rating": 2, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n\nS1.The paper is clearly written and easy to follow.\n\nS2.It studies the well-established problem of trajectory representation learning, aiming to generate effective embeddings for downstream tasks.\n\nS3.The proposed model, SMARTraj^2, is designed to learn trajectory representations from both multi-view and multi-city data.\n\nWeaknesses\n\nW1. All figures in the paper are blurry, making them difficult to interpret.\n\nW2. The paper lacks a detailed discussion on the feasibility and challenges of multi-city learning. Many cities often differ significantly in spatial structures; however, the experiments are limited to two datasets, i.e., Xian and Chengdu, which are both centrally distributed cities. The generalization to cities with different structures remains unclear.\n\nW3. The ablation study does not include an analysis of performance on a single city, making it difficult to understand the contribution of multi-city learning.\n\nW4.  In Section 4.2, the authors claim: “This enables SMARTraj^2 to remain stable in new urban environments without requiring retraining from scratch.” However, this statement is questionable since the model has only been evaluated on two similar cities. It is unclear how it would perform in truly unseen cities.\n\nW5. Incorporating multi-city data increases data volume and computational cost. The authors should provide a thorough analysis of the efficiency of the proposed model and justify the cost-effectiveness if performance gains are not substantial.\n\nW6. There are inconsistencies in experimental settings between SMARTraj^2 and baseline methods, including differences in training epochs and task configurations.\n\nW7. Several relevant and recent baselines are not included, such as:\n\n[1] LightPath: Lightweight and Scalable Path Representation Learning, KDD 2023\n\n[2] RED: Effective Trajectory Representation Learning with Comprehensive Information, VLDB 2024", "was_revised": true, "final_justification": "The authors have provided additional experiments, including the addition of a new baseline, an efficiency study, and an ablation study. Based on these efforts, I would like to raise my scores for Quality and Originality. However, this paper still has several unresolved issues:\n\n1. This paper fails to provide a fundamental explanation for the effectiveness of multi-city modeling in trajectory modeling. In particular, the data sources and structural characteristics vary significantly across different cities. The experiments are conducted using trajectory data from only two structurally similar cities, which is insufficient to demonstrate the general applicability or effectiveness of the proposed multi-city modeling approach.\n\n2. The training time of the proposed model is more than twice that of the baseline, which makes the comparison unfair. Combined with its extremely low efficiency, it becomes evident that the method does not offer any clear advantages when considering the trade-off between performance and efficiency. \n\n3. The authors claim that the model can be applied to new cities without retraining. However, due to its reliance on city-specific information, particularly details related to different road networks, the model fails to achieve effective transferability. In practice, it still requires retraining when deployed in a new city.\n\nBased on these concerns and given the high standards for paper quality at NeurIPS, I will maintain my overall score and recommend rejection.", "rebuttal": "We thank Reviewer ic1N for the constructive feedback. We address each point below:\n\n**W1**. High-resolution versions of all figures will be included in the revised manuscript. We are re-rendering all visuals to ensure clarity and interpretability.\n***\n**W2\\&W4\\&Q1**. To evaluate generalization beyond centrally structured Chinese cities, we are currently conducting experiments on the Porto dataset, which features a decentralized European-style road network. This experiment uses the same model configuration and evaluation protocols. Results will be added to this rebuttal as soon as they are available (expected within the discussion window, by August 6).\n***\n**W3\\&Q2**. We are also conducting an ablation study where the model is trained on a single city only, in order to assess the benefits of multi-city learning. We will update this rebuttal with results within the discussion window (by August 6).\n***\n**W5\\&Q3**. We conducted additional experiments to assess model complexity and runtime efficiency. As shown below, while our method has a larger model size compared to some baselines, its inference time remains within a practical range. These results suggest that our approach is feasible for real-world deployment. A detailed discussion will be included in the revised manuscript.\n| Method       | Model Size (MB) | Train Time (min/epoch) | Inference Time (ms) |\n|--------------|----------------:|-----------------------:|--------------------:|\n| Random       | -               | -                      | 0.374               |\n| Word2Vec     | 8.0             | 0.2                    | 0.404               |\n| Node2Vec     | 7.6             | 0.2                    | 0.328               |\n| Transformer  | 14              | 1.5                    | 0.940               |\n| BERT         | 433             | 3.5                    | 1.260               |\n| Toast        | 27              | 7.2                    | 2.152               |\n| JCLRNT       | 13              | 1.3                    | 0.792               |\n| START        | 94              | 17                     | 4.700               |\n| JGRM         | 33              | 15                     | 4.220               |\n| MVTraj       | 207             | 35                     | 10.560              |\n| Ours         | 477             | 54                     | 11.65               |\n***\n**W6\\&Q4**. We acknowledge that there are differences in training epochs and task configurations between our method and baseline models. These differences arise primarily because baselines are not designed for multi-city representation learning. As a result, they are trained and evaluated on single-city datasets, and generally converge within 30 epochs. In contrast, our method operates across multiple cities and views simultaneously, which introduces greater structural and distributional heterogeneity. To ensure convergence under this setting, we trained for 70 epochs. Nevertheless, we ensured that all models were trained to convergence. \n***\n**W7\\&Q5**. Due to time constraints during the rebuttal phase, we prioritized reproducing RED (VLDB 2024), a recent and strong baseline. As shown in the RED paper, it consistently outperforms LightPath (KDD 2023) across multiple tasks. Thus, RED serves as a strong representative for recent trajectory representation learning approaches. We are currently running RED under our evaluation protocol and will update this rebuttal with results by August 6.\n\nIf time permits and implementation is accessible, we may consider more baselines in the revised manuscript as well.\n***\nWe will post all new experimental results as soon as they are ready (expected within the discussion window, by August 6). We appreciate your patience and believe these additional results will help clarify the strengths and limitations of our approach."}, {"review_id": "KzrexwO8pK", "reviewer": "Reviewer_av9i", "summary": "This paper presents a novel approach to multi-city trajectory representation learning that effectively addresses two key challenges: structural heterogeneity across cities and performance trade-offs between different views/tasks. The proposed SMARTraj2 method demonstrates strong empirical results and represents a meaningful advance in the field. While generally well-executed, some aspects could be strengthened to make the contribution even more impactful.", "questions": "1. Have the authors explored alternative designs for the gating mechanism? For instance, could attention-based gating provide more interpretable weighting across cities/views?\n2. The current approach maintains separate feature extractors per city. For deployment at scale, has the team considered more parameter-efficient approaches (e.g., adapter modules) to handle many cities?\n3. The paper shows cross-city generalization, but does the model maintain strong performance when tested on entirely unseen cities (zero-shot transfer)? This would further demonstrate the method's practical utility.", "limitations": "The trajectory data could potentially reveal sensitive location patterns. Does the method offer any privacy-preserving properties?", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strength:\n1. The dual-component architecture is cleverly designed to handle cross-city generalization. \n2. The experimental design is thorough, with appropriate baselines and multiple evaluation metrics. The ablation studies effectively validate the importance of each component. \n3. The paper provides sufficient implementation details and references to publicly available datasets. The inclusion of hyperparameter sensitivity analysis is commendable. \n\nWeakness:\n\n1. Have the authors explored alternative designs for the gating mechanism? For instance, could attention-based gating provide more interpretable weighting across cities/views?\n2. The current approach maintains separate feature extractors per city. For deployment at scale, has the team considered more parameter-efficient approaches (e.g., adapter modules) to handle many cities?\n3. The paper shows cross-city generalization, but does the model maintain strong performance when tested on entirely unseen cities (zero-shot transfer)? This would further demonstrate the method's practical utility.", "was_revised": true, "final_justification": "The article is particularly good. rebuttal perfectly solved my problem. I suggest accepting this article.", "rebuttal": "We thank Reviewer av9i for the constructive feedback. We address each point below:\n\n**W1\\&Q1**. We explored an attention-based gating design that outputs explicit attention weights over cities or views. While this formulation offers interpretability via attention visualization, it resulted in lower performance and did not yield actionable insights during user studies. In contrast, our personalized gating mechanism—conditioned on both city identity and trajectory context—achieves better empirical results and adaptively captures localized patterns. This dynamic design distinguishes it from static or globally shared gates in prior work.\n***\n**W2\\&Q2**. We acknowledge the scalability limitation of maintaining separate city-specific extractors. We have not yet incorporated parameter-efficient alternatives such as adapters or hyper-networks. We plan to explore these directions in future work and will explicitly mention them in the revised paper. Our envisioned approach is to replace per-city backbones with a shared backbone plus lightweight adapters, which would significantly reduce overhead when scaling to hundreds or thousands of cities.\n***\n**W3\\&Q3**. We recognize the route view is inherently non-transferable due to city-specific road segment IDs. However, the remaining views (GPS, POI, grid) are transferable across cities if similar raw data is available. In preliminary zero-shot experiments (held-out city with no finetuning), our model retains reasonable performance using only transferable views. We will report and expand on this in the revised version, as it demonstrates the practical utility of the method in unseen domains.\n***\n**L1**. We appreciate the concern regarding potential privacy risks. We will expand the 'Limitations' section in the revised manuscript to address: (1) Privacy implications of using trajectory data, including risks of location pattern inference; (2) Possible mitigation strategies such as data anonymization, spatial generalization (e.g., using coarse grids), and privacy-preserving learning (e.g., federated or differential privacy); (3) Broader ethical implications, including both potential misuse (e.g., surveillance) and positive societal applications (e.g., urban planning, traffic safety)."}, {"review_id": "tbAhX8cKmm", "reviewer": "Reviewer_e4tw", "summary": "The paper introduces SMARTraj^2, a novel method for multi-view spatio-temporal trajectory representation learning designed to address the challenges of generalizing across diverse urban scenes. The authors identify two critical issues in existing approaches: multi-city structural heterogeneity (disparities in urban layouts leading to disjoint embedding spaces) and the amplified seesaw phenomenon (performance trade-offs between cities, views, and tasks). To tackle these, SMARTraj^2 employs a feature disentanglement module to separate domain-invariant and domain-specific features using orthogonality constraints, and a personalized gating mechanism to dynamically stabilize contributions across cities, views, and tasks. Extensive experiments on real-world datasets (Chengdu and Xi’an) demonstrate that SMARTraj^2 outperforms state-of-the-art baselines in tasks like road label classification, travel time estimation, and destination prediction, while maintaining robustness across heterogeneous urban environments.", "questions": "1. This work implements a method to integrate data from multiple cities to train a universal model. However, does this approach increase the complexity of the model? Is it feasible for practical applications under this level of complexity? \n2. This paper proposes a method for training across multiple cities. Is this method scalable? Please provide a theoretical explanation.\n3. The paper selects four downstream tasks to evaluate the effectiveness of the representation, but it does not explain the reasons for the selection. Can these downstream tasks comprehensively assess the capability of the representation? \n4. Could the authors provide a detailed explanation of the role of gamma in the Personalized Gating Mechanism? (It seems that there might be an error in the x-axis of Figure 3(a) in the appendix.)", "limitations": "The authors could add a dedicated \"Limitations\" subsection to discuss limitations (e.g., reliance on multi-view data, seesaw phenomenon mitigation) and societal impacts (e.g., potential misuse for surveillance).", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths\n1) Quality: The paper is technically sound, with well-designed experiments and comprehensive ablation studies validating the contributions of each component (e.g., feature disentanglement and gating mechanism). The use of real-world datasets and multiple downstream tasks strengthens the empirical evaluation.\n2) Clarity: The writing is clear, and the methodology is well-structured. Figures and tables effectively support the narrative. The problem statement, challenges, and solutions are logically connected.\n3) Significance: The work addresses a critical gap in trajectory representation learning by enabling cross-city generalization, which is essential for real-world applications like transportation and urban planning. The proposed solutions (disentanglement and gating) are novel and impactful.\n4) Originality: The paper is the first to explicitly tackle multi-city, multi-view trajectory learning with a focus on stability and generalization. The personalized gating mechanism and orthogonality constraints are innovative contributions.\n\nWeaknesses: \n1) Complexity: This work implements a method to integrate data from multiple cities to train a universal model. However, does this approach increase the complexity of the model? Is it feasible for practical applications under this level of complexity? \n2) Scalability: This paper proposes a method for training across multiple cities. Is this method scalable? Please provide a theoretical explanation.\n3) Capability: The paper selects four downstream tasks to evaluate the effectiveness of the representation, but it does not explain the reasons for the selection. Can these downstream tasks comprehensively assess the capability of the representation? \n4) Presentation: Could the authors provide a detailed explanation of the role of gamma in the Personalized Gating Mechanism? (It seems that there might be an error in the x-axis of Figure 3(a) in the appendix.)", "was_revised": true, "final_justification": "Thank you for the detailed rebuttal and the additional analyses. \n\nAfter reviewing the paper, the comments from other reviewers, and the author rebuttal, I find that the proposed approach reasonably addresses structural differences across cities while preserving transferability.\n\nThe supplementary experiments, especially under varying numbers-of-cities scenarios, strengthen the evidence for the method’s adaptability. The conceptual generalizability is clear, and the problem tackled is both challenging and practically important.\n\nOn balance, my assessment remains positive, and I believe the work makes a meaningful contribution to the area.", "rebuttal": "We thank Reviewer e4tw for the valuable comments. We address each point below:\n\n**W1\\&Q1**. We conducted additional experiments to assess model complexity and runtime efficiency. As shown below, while our method has a larger model size compared to some baselines, its inference time remains within a practical range. These results suggest that our approach is feasible for real-world deployment. A detailed discussion will be included in the revised manuscript.\n| Method       | Model Size (MB) | Train Time (min/epoch) | Inference Time (ms) |\n|--------------|----------------:|-----------------------:|--------------------:|\n| Random       | -               | -                      | 0.374               |\n| Word2Vec     | 8.0             | 0.2                    | 0.404               |\n| Node2Vec     | 7.6             | 0.2                    | 0.328               |\n| Transformer  | 14              | 1.5                    | 0.940               |\n| BERT         | 433             | 3.5                    | 1.260               |\n| Toast        | 27              | 7.2                    | 2.152               |\n| JCLRNT       | 13              | 1.3                    | 0.792               |\n| START        | 94              | 17                     | 4.700               |\n| JGRM         | 33              | 15                     | 4.220               |\n| MVTraj       | 207             | 35                     | 10.560              |\n| Ours         | 477             | 54                     | 11.65               |\n***\n**W2\\&Q2**. Our framework is designed to be modular and scalable. The dual-channel architecture enables seamless extension to additional cities by adding new city-specific modules without retraining the entire model. This design supports incremental scalability in both theory and practice. We will clarify this point and provide further explanation in the revised paper. \n***\n**W3\\&Q3**. We follow prior work [7, 24] in selecting four well-established downstream tasks, which cover both short-term and long-term trajectory understanding. Specifically, they evaluate different dimensions of the learned representation: classification, regression, and spatial prediction at both road-level and grid-level. These tasks provide a comprehensive assessment of the model’s ability to generalize and reason across spatial and temporal patterns. We will make this rationale explicit in the revision.\n***\n**W4\\&Q4**. Gamma is a temperature parameter that controls the sharpness of the gating function. A higher gamma increases selectivity, encouraging the gate to favor either shared or city-specific features more decisively. Conversely, a lower gamma leads to smoother blending. We will expand this analysis in the revised manuscript and verify the accuracy of Figure 3(a)'s x-axis annotation.\n***\n**L1**. We agree and will include a dedicated “Limitations” section in the revised paper. It will discuss: (1) Technical limitations, such as the reliance on multi-view data and the challenge of balancing transferability and specialization (i.e., the seesaw phenomenon); (2) Scalability concerns, such as parameter growth in multi-city settings (see also W2); (3) Societal impact, including potential risks such as location privacy leakage and surveillance, as well as mitigation strategies like anonymization, spatial aggregation, or privacy-preserving learning frameworks."}]

Author rebuttal:

Meta-review: The paper received 3 accept and 1 reject ratings. Reviewer praised for the approach of multiple-city modeling while concerning about the fairness of experiments, as well as the presentation. The authors provided a rebuttal. Most of those concerns were addressed while reviewers still have concerns, regarding the number of training epochs for baselines (fewer epochs). AC tends to trust the authors that those baselines, since they are single city based modeling, trains faster and converges within fewer epochs. AC recommends to accept this paper. Please incorporate the suggestions given by reviewers, especially  for the number of epochs in baseline experiments.  If it converges within fewer epochs, please still train them with more epochs so the audiences can see the performance is truly saturated.
Decision: Accept (poster)