Improving intelligent public transport systems for smart cities
Creators: name: MA, Jiaman
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 460502
subject: Data mining and knowledge discovery
classificationCode: 461103
subject: Deep learning

description: With the recent growth in urban populations, traffic pressures on roads have become more severe in many cities around the world. As an efficient and environment-friendly alternative to motorized urban travel, public transport systems are vital for urban residents' commuting. Therefore, improving the serviceability of public transportation systems and attracting passengers to utilise them is an important factor in the development of smart cities and smart traffic systems.

Although the public transport system has good characteristics of efficiency, such as possibility to avoid traffic jams and enabling to perform work related activities while using public transport, some problems related to mass transportation lead citizens to choose private transport, as an alternative, for their travel purposes. Therefore, this thesis aims to solve some of the challenges of public transport systems, to satisfy the requirement of passengers, and in order to encourage citizens to choose public transport for both their short and long travels.

The problems of public transport systems can be divided into two parts. First, the unreliable serviceability of existing transport systems. For example, long waiting times and crowdedness at rush hours usually discourage urban residents from choosing public transportation. Second, the imperfection of existing transport network, such as unavailability of suitable lines for remote areas and long travel or transfer times from origins to destinations. In summary, the two main problems, the travel time and passenger demand planning in both developed and developing city areas, are focuses of this thesis in order to achieve the goals of intelligent public transport systems (IPTS). The proposed models help urban managers to satisfy the various passenger demands in different areas, and hence increase the efficiency of the transport systems.

A public transport system in the urban areas can be represented as a graph. It comprises two main components: stations (nodes) and paths between adjacent stations (links). We can transform the problem of improving public transport systems to one that addresses how to arrange the nodes and link them reasonably for updating and designing the timetable, paths and stations efficiently. Currently, the planning of transport systems in most cities depends on the designers' experience and surveys from urban residents. Despite the substantial time consumption and cost incurred for the survey process, the passenger demand analysis based on surveys has not managed to reflect the fast development of urban areas. This leads ineffectiveness and inaccuracy in developing IPTS. Based on the heterogeneous data generated in cities, such as vehicle trajectories, social-media check-ins and telephone GPS data, we focus on changing and developing the existing transport networks from the perspectives of both stations and paths. For paths, we focused on the following: 1) providing accurate travel time information for current existing developed transit routes with sufficient historical records to predict future travel times; 2) providing travel times of the routes with sparse travel records (in suburbs) and the new routes under design (no historical travel records). For stations, we focused on the following: 1) discovering popular origins and destinations in city areas as the potential stations; and 2) predicting the volumes of passenger flows between each origin and destination pair.

Impacted by many complex real-time traffic factors, such as traffic conditions, weather and passenger numbers at each stop, travel times of public transportation are challenging to predict accurately under different traffic conditions, particularly when traffic congestion occurs. For predicting travel times of existing transit paths with full historical records (hereafter, we denote this as fully-recorded transits), we propose a novel segment-based approach to predict travel times of public transport transits. Our approach can automatically learn the travel patterns under different traffic conditions and model the transit and dwelling times from independent underlying patterns based on heterogeneous traffic data. The proposed model reveals the impact of real-time traffic conditions on public transportation and improves the accuracy of travel time prediction.

Developing a public transport system, designing new routes and updating the routes in the suburbs based on travel times, are frequently required tasks for maintaining an efficient transport system. However, the travel times of transit routes with sparse or no historical records are difficult to predict. The challenges come from the lack of reliable historical travel records and the intricate and independent spatial-temporal travel patterns. We propose a multi-attention graph neural network based approach to predict city-wide travel times using limited data. We first discover the geo-structures of routes and represent each travel segment in the transport network to form a novel graph view by a proposed density-based representation algorithm. We then propose a graph neural network with a self-attention block focusing on global and local spatial neighbors to learn the complex latent travel patterns from fully-recorded routes for predicting the routes with little or no historical data.

A station in public transportation usually serves an area with relatively dense human flows within a walking range. For discovering potential stations, it is essential to mine popular origins and destinations in the city areas under some constraints. With the characteristics of stations, we propose a concept of regions of interest within walking size range as WROIs, and a model to discover the WROIs efficiently. We address this problem by a novel hierarchical graph-based clustering approach. Specifically, we first construct a city network graph using heterogeneous data. In the graph, we discover the spatial-temporal features of locations, the human mobility demands around them, and construct a novel hierarchical, weighted edge matrix to represent the correlations among locations. Based on the graph, we propose a hierarchical graph-based clustering algorithm to discover dense sub-graphs of a city network as WROIs.

Predicting passenger flows among stations is crucial to arrange the time tables and routes passing each station.  These demands in a public transport system can be represented as an origin-destination (OD) matrix. The main challenges of OD crowd flow prediction are the diverse flow patterns across city networks and data sparsity. To solve these problems, we propose a Multi Attention 3D Residual Networks (MAThR) to predict city-wide OD crowd flows. In particular, a multi-component 3D residual structure is constructed with a novel global self-attention mechanism for dynamically aggregating the OD spatial-temporal dependencies simultaneously for temporal periods. In summary, this thesis provides novel contributions on how to improve the serviceability of public transportation from the views of paths and stations.