Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-21-081 JUNE 2022

Effectiveness of TMC AI Applications 

in Case Studies 
FOREWORD

The Turner-Fairbank Highway Research Center performs advanced research on several areas of 

transportation technology for the Federal Highway Administration (FHWA). The Office of 

Operations Research and Development (HRDO) focuses on improving operations-related 

technology through research, development, and testing. 

This report presents research about the potential for using artificial intelligence (AI) algorithms 

for highway traffic incident detection, specifically an AI-based incident-detection framework 

that can leverage large-scale sensor data along with advanced learning algorithms to improve the 

performance of incident detection. This research project was sponsored by the FHWA’s HRDO. 

This report may be a useful reference for incident management teams, traffic operators, and those 

interested in traffic incident detection.

Brian P. Cronin, P.E. 

Director, Office of Operations Research and 

Development 

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

(USDOT) in the interest of information exchange. The U.S. Government assumes no liability for 

the use of the information contained in this document. 

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document. 

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement. 

Recommended citation: Federal Highway Administration, Effectiveness of TMC AI Applications 

in Case Studies (Washington, DC: 2021) https://doi.org/10.21949/1521668
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-21-081

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Effectiveness of TMC AI Applications in Case Studies

5. Report Date

February 2022

6. Performing Organization Code

7. Author(s) 

H. Yang (ORCID: 0000-0003-2808-8852), M. Cetin (ORCID: 

0000-0003-2003-9343), Z. Wang, Z. Huang, S. Nallamothu 

(ORCID: 0000-0002-7457-3704), P. Huang

8. Performing Organization Report No.

9. Performing Organization Name and Address

Leidos, Inc.

11251 Roger Bacon Drive

Reston, VA 20190

Old Dominion University

4700 Elkhorn Avenue

Norfolk, VA 23529

10. Work Unit No.

11. Contract or Grant No.

DTFH61-16-D00030

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration 

1200 New Jersey Avenue SE

Washington, DC 20590

13. Type of Report and Period Covered

Final Report; January 2020–December 2020

14. Sponsoring Agency Code

HRDO-10

15. Supplementary Notes

The contracting officer’s technical representative for this report was Peter Huang (HRDO-10).

16. Abstract

Traffic incident detection is a crucial task in traffic management centers (TMCs) that typically manage large 

highway networks with limited staff. An effective automatic incident-detection approach could benefit TMCs by 

helping to report abnormal events in a timely and accurate manner and optimize operating resources. During the 

past decades, researchers have made significant progress in developing such automatic approaches. Nevertheless, 

the majority of the developed approaches have shown limited success in the field, largely because of concerns 

about their often-costly false alarms (e.g., misdispatching response teams to a nonexistent incident). Fortunately, 

recent advances in artificial intelligence (AI) are expected to provide opportunities for improving conventional 

TMC operations. This project aimed to propose an AI-based incident-detection framework that can leverage 

large-scale sensor data along with advanced learning algorithms to improve the performance of incident detection. 

Researchers investigated the generic algorithmic problems in designing a detection approach and emphasized the 

architecture of the AI-based detection framework by including learning and evolving capabilities. The proposed 

framework was assessed with a fully controlled experiment in simulation that consisted of numerous traffic and 

incident scenarios. The results indicated that the proposed AI-based framework achieved higher detection rates, 

lower false alarm rates, and shorter time to detect the incidents in the studied scenarios than conventional 

approaches. Some extensions of the proposed framework are also discussed.

17. Key Words

Artificial intelligence, traffic incident detection, 

sensor data, traffic management centers, neural 

networks

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

http://www.ntis.gov

19. Security Classif. (of this report) 

Unclassified

20. Security Classif. (of this page) 

Unclassified

21. No. of Pages

48

22. Price

N/A 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.
ii
iii

TABLE OF CONTENTS

CHAPTER 1. INTRODUCTION ................................................................................................ 1

CHAPTER 2. LITERATURE REVIEW .................................................................................... 3

Methods Comparing Static or Dynamic Thresholds ........................................................... 3

Methods Based on Statistical or ML Approaches ................................................................ 4

Summary.................................................................................................................................. 5

CHAPTER 3. INCIDENT-DETECTION PROBLEM ............................................................. 7

CHAPTER 4. METHODOLOGY ............................................................................................. 11

Conceptual Framework of an AI-based Incident-Detection Approach ........................... 11

Specification of Model M1 with Neural Networks .............................................................. 15

Benchmark Algorithms ........................................................................................................ 16

CHAPTER 5. EXPERIMENTS TO TEST THE PROPOSED AI APPROACH ................. 19

Experimental Design ............................................................................................................. 19

Performance Criteria............................................................................................................ 20

CHAPTER 6. RESULTS ............................................................................................................ 23

CHAPTER 7. FUTURE WORK ............................................................................................... 27

CHAPTER 8. CONCLUSION ................................................................................................... 29

APPENDIX. PERFORMANCE COMPARISONS OF DIFFERENT MODELS UNDER 

DIFFERENT SCENARIOS ....................................................................................................... 31

REFERENCES ............................................................................................................................ 39
iv

LIST OF FIGURES

Figure 1. Illustration. Conceptual illustration of incident occurrence on highways. ...................... 7

Figure 2. Illustrations. Different modeling strategies and outcomes. ............................................. 8

Figure 3. Illustration. An AI modeling framework with memory units and learning ability. ...... 12

Figure 4. Illustration. Memory unit with the archive of historical information for reference. ..... 13

Figure 5. Illustration. Decision tree of CA No. 7. ......................................................................... 17

Figure 6. Illustration. Simulation in VISSIM. .............................................................................. 20

Figure 7. Graphs. Possible prediction results................................................................................ 21

Figure 8. Graphs. Performance comparison of different models in terms of FPs, TPs, and 

TTD (0.3 mi, near upstream). .............................................................................................. 25

Figure 9. Graphs. Performance comparison of different models in terms of FPs, TPs, and 

TTD (0.5 mi, near upstream). .............................................................................................. 31

Figure 10. Graphs. Performance comparison of different models in terms of FPs, TPs, and 

TTD (1.0 mi, near upstream). .............................................................................................. 32

Figure 11. Graphs. Performance comparison of different models in terms of FPs, TPs, and 

TTD (0.3 mi, near middle). ................................................................................................. 33

Figure 12. Graphs. Performance comparison of different models in terms of FPs, TPs, and 

TTD (0.5 mi, near middle). ................................................................................................. 34

Figure 13. Graphs. Performance comparison of different models in terms of FPs, TPs, and 

TTD (1.0 mi, near middle). ................................................................................................. 35

Figure 14. Graphs. Performance comparison of different models in terms of FPs, TPs, and 

TTD (0.3 mi, near downstream). ......................................................................................... 36

Figure 15. Graphs. Performance comparison of different models in terms of FPs, TPs, and 

TTD (0.5 mi, near downstream). ......................................................................................... 37

Figure 16. Graphs. Performance comparison of different models in terms of FPs, TPs, and 

TTD (1.0 mi, near downstream). ......................................................................................... 38
v 

LIST OF TABLES

Table 1. Representative work on incident detection. ...................................................................... 6

Table 2. Average predictive performance without specifying incident locations and traffic 

demands. .............................................................................................................................. 24
vi 

LIST OF ACRONYMS

AI artificial intelligence

ANN artificial neural network

APID all-purpose incident detection

ARIMA autoregressive integrated moving average 

CA No. 7 California algorithm No. 7 

CNN convolutional neural network 

DBN deep belief network 

DL deep learning 

DR detection rate 

FAR false alarm rate

FN false negative

FP false positive

GPS global positioning system 

HIOCC high occupancy 

KNN K-nearest neighbors 

LSTM long short-term memory

ML machine learning

TID traffic incident detection

TMC traffic management center

TP true positive 

TTD time to detect

SND standard normal deviate 

SVM support vector machine 

vph vehicles per hour
1 

CHAPTER 1. INTRODUCTION

Traffic incidents are one of the critical factors that frequently disrupt daily operations of highway 

systems. It has been estimated that they account for nearly a quarter of all delays on the highway 

system in the United States (USDOT 2006, 2019). In addition, they can significantly impact the 

safety of both motorists and incident responders by exposing them to the risk of secondary 

incidents (Yang et al. 2018). Thus, incident-management agencies are actively working on 

various strategies with the common goal to detect, respond to, and remove incidents and restore 

traffic operations as safely and quickly as possible. Many of the implemented strategies, such as 

quick clearance laws, have shown great benefits in improving safety, mobility, and motorist 

satisfaction (Fries et al. 2012; Rensel et al. 2018). Among all the strategies, traffic incident 

detection (TID) is one of the most important tasks. Timely and accurate detection of incident 

occurrence is critical to time-sensitive incident-management plans. Delays in detecting incidents 

may increase the severity of victim injuries and cause heavier congestion. Meanwhile, falsely 

detecting incidents increases the cost of operating incident-management programs. 

TID is challenging because of the randomness of incident occurrence time and location. Many 

traffic management centers (TMCs) still heavily rely on human-based detection approaches, such 

as visual checking by TMC operators reviewing highway surveillance cameras (Williams and 

Guin 2007). Human detection requires a large share of the workforce in TMCs to provide 24/7 

examination over a broad road network. This demand often leads to a shortage of operators 

(e.g., during holidays and weekends) and high costs for recruiting and overtime pay for staff. 

These issues have led to a growing concern among transportation agencies that human detection

is not capable of continuously providing the needed detection functionality given the limited 

resources. This concern has generated increased interest in automating the incident-detection 

process. Over the past few decades, several automatic detection algorithms have been developed 

as part of the solutions for intelligent transportation systems. These algorithms, ranging from 

simple comparative approaches to advanced machine learning (ML) methods, have emerged as 

viable alternatives for detecting highway incidents with the access of various sensor data (Dudek 

et al. 1974; Masters et al. 1991; Dia and Rose 1997; Samant and Adeli 2000; Jin et al. 2001; Yao 

et al. 2014; Wang et al. 2016). Despite promising features, early studies showed that these 

algorithms were often disabled or ignored by many TMCs because of concerns about the 

unacceptable false alarm rates and weak transferability among highways (Williams and 

Guin 2007). For example, a nationwide survey found that TMCs consider no more than 10 false 

alarms per day to be acceptable, but many existing algorithms cannot meet this expectation 

(Williams and Guin 2017). The failure of these algorithms is attributable to many factors, such as 

restricted modeling assumptions and time-consuming calibration efforts. 

With fast-growing data and computational capabilities, the advancements of artificial 

intelligence (AI) have drawn significant attention in various fields, including the transportation 

sector. For example, many of the core functions of emerging autonomous vehicles (such as 

object detection and motion planning) have been built on the success of AI solutions. It is 

expected that TMCs can also benefit from AI in addressing safety and congestion challenges 

across the large highway networks they manage. The use of AI is largely facilitated by the 

massive amount of firsthand data that TMCs can access through their numerous sensor nodes in 

road networks. Although promising, AI techniques have not been fully tested in the context of 
2 

TMC operations such as incident detection and flow prediction. As such, the primary objective 

of this project was to examine the potential of using AI algorithms for highway TID. It intended

to leverage the power of traffic-simulation models to quantitatively assess the performance of an 

AI-based detection approach in a fully controlled experimental condition. The simulation model 

enabled the creation of numerous incident cases and traffic conditions that cannot be easily 

collected in the field and helped to overcome many other real-world issues, such as the lack of 

precise incident data and sparse traffic measurements due to larger detector spacing. The 

algorithmic problem of incident detection is discussed to raise awareness of improving existing 

methods. This project demonstrates the architecture of the detection approach based on a simple

AI-based framework. It contributes to the literature by demonstrating the advantages of including 

learning and evolving capabilities in the developed AI-based framework.
3 

CHAPTER 2. LITERATURE REVIEW

Automatic TID has been discussed for many years. There has been extensive research on 

developing detection methods using different data and techniques. In general, the representative 

studies can be grouped into two main categories: methods comparing static or dynamic 

thresholds and methods based on statistical or ML approaches. 

METHODS COMPARING STATIC OR DYNAMIC THRESHOLDS

Comparative methods assume that traffic metrics such as volume, occupancy, and speed will 

change after an incident. They compare observed traffic metrics with predefined threshold 

values. Once the metrics exceed such thresholds, an incident is reported. Typical algorithms 

include decision tree algorithms, pattern recognition algorithms, and all-purpose incident 

detection (APID) algorithms. 

Decision tree algorithms organize predefined thresholds in a hierarchal structure and provide 

different output states on leaf nodes. One of the classic algorithms is California algorithm No. 7 

(CA No. 7). CA No. 7 derives traffic metrics parameters via the observed occupancy data from 

two adjacent sensors and compares such parameters with predefined values (Payne and 

Tignor 1978). Later, an APID algorithm was developed to improve the transferability of 

CA No. 7 and expand the major elements of the California algorithms into a comprehensive 

structure (Masters et al. 1991; Li et al. 2016; Saifuzzaman et al. 2018). Collins et al. (1979) 

introduced the high occupancy (HIOCC) algorithm, which monitors the high-frequency detector 

data for changes over time. They conducted the field test with data from two highways in 

London and proved that HIOCC can work well under congested conditions. Other traffic metrics, 

such as travel time and vehicle speed, have also been used to detect incidents. For example, the 

Transport and Road Research Laboratory developed a pattern recognition algorithm (Collins et 

al. 1979) in which vehicle speeds were estimated by observing travel times between loop 

detectors. Once vehicle speeds exceeded the pre-established threshold values for a preset number 

of consecutive time steps, an incident alarm was triggered.

Similarly, researchers have developed methods based on statistical metrics to detect potential 

incidents, including the standard normal deviate (SND) algorithm and Bayesian algorithms 

(Dudek et al. 1974; Tsai and Case 1979; Li et al. 2017). The SND algorithm computes the SND 

of the traffic-control measure (such as occupancy), and if the SND exceeds a predefined 

threshold, an incident is reported. Bayesian algorithms introduce the relative difference of 

occupancies used in CA No. 7 approaches and derive the conditional probability using Bayesian 

statistics. These algorithms are often static and cannot be easily transferred to different scenarios. 

Besides simply using occupancy data, some models compare other traffic variables, such as 

speed and travel time, with predicted outputs via algorithms such as autoregressive integrated 

moving average (ARIMA) to prompt a potential incident report (Ahmed and Cook 1979, 

1980, 1982). The ARIMA model takes advantage of the temporal correlation between traffic 

variables measured in current time step t and previous time step t-k and learns the normal pattern 

of such a relationship under incident-free conditions. Nevertheless, the reliability of existing 

models remains an issue in scenarios like recurrent congestion. Some researchers also smoothed 
4 

the observed traffic variables and used weighted average values as the model input to reduce 

potential false alarms (Wang et al. 2016). 

Researchers have also sought data from sources other than traffic sensors, such as the global 

positioning system (GPS) and images, for incident detection. For instance, the Autoscope 

incident detection algorithm was improved by including ancillary information provided by video 

detection (Michalopoulos 1991; Michalopoulos et al. 1993). By using travel time and other 

spatial traffic measures collected by probes, information about traffic conditions can be archived 

with higher accuracy and precision (Asakura et al. 2017). For example, Hellinga and 

Knapp (2000) collected travel time data based on transponder readers to detect incidents. If the 

travel time exceeded the predefined thresholds, an incident was detected. Recently, Asakura 

et al. (2017) archived travel times and the number of probe vehicles passing through a bottleneck 

to detect potential incidents. These studies suggest that no single data source or algorithm will 

always work for different traffic and incident scenarios. 

METHODS BASED ON STATISTICAL OR ML APPROACHES

Many learning algorithms consider incident detection as a task to construct a classification model 

according to traffic metrics. Approaches such as ML, deep learning (DL), and clustering have 

been applied to classify traffic states with and without incidents (Fries et al. 2012). 

Artificial neural networks (ANNs) have been widely studied to detect freeway incidents, and 

their performance has been promising (Cheu and Ritchie 1995; Dia and Rose 1997; Lee 

et al. 2004). Many models have been developed, such as multilayer feed-forward neural network, 

constructive probabilistic neural network, and probabilistic neural network (Jin et al. 2001; Wen 

et al. 2001). Other ML algorithms, such as support vector machine (SVM), particle swarm 

optimization, and random forest, have also been applied to detect traffic incidents. For example, 

Yao et al. (2014) used the tabu search algorithm to optimize the parameters of SVM to detect

incidents. Local optimums can be avoided. In addition, ML algorithms have been coupled with 

ANNs to improve the detection performance (Zhao et al. 2018). Kinoshita et al. (2015) 

introduced a traffic state model based on a probabilistic topic model to describe traffic states for 

a variety of roads. 

Some researchers have examined the use of DL approaches for incident detection. DL uses 

multiple-layer architectures or deep architecture of neural networks to extract inherent features in 

data of different complexities and can represent them without prior knowledge, which offers 

promising functions for TID. For example, El Hatri and Boumhidi (2018) proposed a novel fuzzy 

DL-based detection method that considers the spatial and temporal correlations of traffic flow. 

The fuzzy logic is introduced to avoid the slow convergence rate and trapping by local optimums 

during tuning learning parameters. Zhang et al. (2018) used deep belief network (DBN) and long 

short-term memory (LSTM) to detect traffic accidents from social media data. DBN outperforms 

SVM and ANNs when processing Twitter data and matching tweets to nearby abnormal traffic 

data. LSTM does not perform well because it depends on sequential information, and words 

(token) in tweets are not sequentially organized well. When using DL approaches, the detection 

performance may improve but the relative time and space complexity will increase, so more 

computing resources will be needed for real-time implementation. Such complex models are also 

prone to overfitting issues. 
5 

With the growth of social media, crowdsourcing data, such as Twitter posts, have also been 

examined. For example, researchers extracted information from social media posts using natural 

language processing algorithms, mapped the data into the high-dimensional vectors in the feature 

space, and classified incident patterns on temporal and spatial dimensions (Nguyen et al. 2015; 

Gu et al. 2016; Salas et al. 2017). Gu et al. (2016) proposed a methodology to crawl, process, and 

filter tweets that are accessible by the public for free. Tweets were acquired from Twitter using 

the REST API in real time. However, relevant social media data are often limited by time and 

locations and can serve only as a supplement for incident detection because many incidents were 

not described in a timely or precise manner by social media sources. 

Table 1 provides details of representative studies, including conventional approaches and ML 

approaches. The table lists information regarding data, method, variables, detection rate (DR), 

time to detect (TTD), false alarm rate (FAR), and complexity. 

SUMMARY

The studies described examined the feasibility of detecting incidents with different types of data 

sourced from loop detectors, vehicle trajectories, images, and social media data. However, 

several issues have not been well addressed. One concern is the overfitting issue. Complex 

models, such as DL approaches, can outperform comparative approaches with training scenarios

but are prone to inferior performance when transferred to other scenarios. In addition, incident 

detection under light volume remains a challenging issue because no comparable traffic pattern 

changes can be observed and used to prompt incident reporting. In addition, the unbalanced 

dataset applied in the tuning process of models such as SVM is often problematic. The lack of 

accurate incident information limits the training performance of these models. Another major 

concern is the models’ inability to learn from historical information. Humans can avoid 

continuously making the same mistakes, but many models with fixed mathematical structures or

decision rules cannot do so if the same input data are given. Updating and learning ability is 

needed to improve model performance. Thus, this project aimed to extend existing efforts by 

introducing an AI-based framework for enhancing predictive performance in incident detection.
6 

Table 1. Representative work on incident detection. 

Reference

Data 

(training/test size, time interval, 

location) Method Variables 

DR

(%) 

TTD

(s)

FAR

(%) Complexity

Stephenedes and 

Chassiakos 1993 

I–35W, Minneapolis, MN Filtering, 

CA No. 7 

Occupancy 93.00 244.00 0.500* Simple

Cheu and Ritchie 

1995 

Simulated data with 100 incidents, 

30-s interval, SR 91 Riverside 

Freeway, California 

ANN Volume, occupancy at 

both upstream and 

downstream detectors

21.00 60.00 0.127* Moderate 

Adeli and 

Samant 2000 

1 incident over a period of 150 min, 

30-s interval, simulated freeway

ANN Feature extraction of 

occupancy and volume 

100.00 47.80 1.200* Moderate 

Jin et al. 2001 45 incidents, 30-s interval, I–880, 

California 

ANN Volume, occupancy, 

speed

86.96 228.00 0.200* Moderate 

Yao et al. 2014 304 incidents, 30-s interval, 

500–700-m interval, April 16–20, 

2012, Liaoning, China 

SVM Weather, time, occupancy, 

volume 

95.70 72.60 4.820* Moderate 

Gu et al. 2016 322 incidents, 5-min interval, Twitter 

data, January–July 2013, Japan 

DSAE GPS, three layers 79.80 NA 0.040ǂ Moderate 

Asakura et al. 

2017 

Probe data, 1-min interval, April–July 

2016, Iowa 

SND, 

outlier 

detection

Speed, probe data 54.10 887.00 0.043ǂ Simple

El Hatri and 

Boumhidi 2018 

30 incidents, 100-s interval, simulated 

data via SUMO

DSAE Traffic flow count 98.23 192.44 0.240ǂ Complex 

Zhao et al. 2018 138 incidents, 1,518 incident-free 

cases, Chongqing, China 

LVQ, fuzzy 

logic

Volume, occupancy, 

speed, meteorological 

parameters

96.50 152.40 0.210* Moderate 

Zhang et al. 2018 584,000 geotagged tweets in northern 

Virginia, 2,420,000 tweets in New 

York, NY from January to December 

2014 

DBN, 

LSTM

Token features extracted 

from tweets 

95.00 NA 30.000ǂ Complex 

*FAR was calculated based on the number of false alarm cases divided by the total number of instances; ǂFAR was calculated based on the number of false 

alarm cases divided by the total number of nonincident instances. 

DSAE = deep sparse autoencoder; LVQ = learning vector quantization; NA = not available; SUMO = simulation of urban mobility.
7 

CHAPTER 3. INCIDENT-DETECTION PROBLEM

As illustrated in figure 1, suppose an incident occurred at location s and time step t. This incident 

may lead to congestion due to lane closure. From the perspective of TMCs, one key task is the 

timely detection of the incident. Data sources such as surveillance cameras and sensor systems 

are often used to detect the incident. Among these data sources, data from loop detectors are 

frequently used. The feasibility of using loop detector data for incident detection largely depends 

on the hypothesis that incident occurrence will be indirectly reflected by the fluctuation of traffic 

conditions. Thus, the generic problem of incident detection becomes the analytics of the changes 

in detector measurements. Equation 1 provides a high-level generalization of the detection

problem. 

(1) 

Where: 

Y = prevailing traffic condition with (y = 1) or without (y = 0) incident occurrence. 

X = the detector measurements. 

f() = a specific modeling approach that associates the detector measurements with the

prevailing traffic condition. 

By specifying appropriate model structure and conducting model calibration and validation, a 

final well-tuned model Mx:f̂

(X | α) can be established, where f̂() represents the finalized model 

with a calibrated parameter set α. Depending on the structure of f̂() and the number of elements 

in α, the model complexity and required computational resources will be different. The tuned 

model Mx is expected to be as efficient and accurate as possible.

Source: FHWA. 

Figure 1. Illustration. Conceptual illustration of incident occurrence on highways. 

Other than the inherent restrictions of each model Mx, a model’s predictive performance will also 

be affected by its implementation strategy. Two typical strategies can be considered: 

continuously implementing a tuned model and periodically updating a model during 

implementation. The first strategy is illustrated in figure 2-A. Once model M1 has been tuned, it 
8 

is deployed continuously. Such a strategy does not consider any resultant error that the model 

may have. Suppose it made a false prediction at time step ti. It will continuously repeat the same 

false prediction (figure 2-C) at time step tj if the input data Xtj is similar to Xti. If the prediction 

errors keep occurring, an update of the model may be necessary. The second implementation 

strategy is illustrated in figure 2-B. The deployed model M1 is reviewed periodically 

(e.g., weekly, monthly). If the performance of the deployed model raises concerns, it is updated 

with additional calibration efforts. Within an updating time window, model M1 (figure 2-D) will 

still repeat the same false prediction at time step ti with the same input data Xti. If an updated 

model M2 is implemented in the second time window, it will produce new prediction result R̂tj at 

time step tj. If the updating efforts have successfully addressed the error issues observed in the 

previous time window, R̂tj is likely to be improved. Otherwise, it may still be subject to similar 

errors because of the limited improvements of the modeling performance. Implementing updated 

models periodically has potential benefits in terms of error reduction. However, this strategy may 

not be computationally practical. One major concern is the updating frequency. Also, adding one 

historical record (e.g., Xti) to a large training dataset may not update a model much in terms of 

predictive performance because the influence of a single record among a large amount of training 

data will be limited when calibrating model parameters (unless it is an extreme case). Thus, the 

value of adding Xti to the training dataset is not substantial. In real-time application at a TMC, a 

stable approach that does not require frequent updates will be more likely to facilitate operators’ 

use and management. 

Source: FHWA. 

Figure 2. Illustrations. Different modeling strategies and outcomes.
9 

To augment the value of any historical false predictions (e.g., Rti), it is helpful to carefully 

examine and leverage the use of the relevant traffic condition measurements Xti. An experienced 

TMC operator that falsely flagged a traffic condition (e.g., in a case that never happened before) 

is highly likely to learn from the recent failure to improve subsequent judgments in similar traffic 

conditions. Thus, a data-driven approach with similar human intelligence is expected to iterate 

and improve during the implementation process.

11 

CHAPTER 4. METHODOLOGY

CONCEPTUAL FRAMEWORK OF AN AI-BASED INCIDENT-DETECTION 

APPROACH

The previous chapter discussed the problems associated with developing and implementing an 

incident-detection model. A fine-tuned model with an acceptable implementation strategy will 

facilitate the detection of incidents. However, any specific model will have certain capabilities

and deficiencies. Modelers often hope to maximize a model’s capabilities while reducing its 

deficiencies. Nonetheless, because of a number of factors, such as outliers, incomplete 

information, model assumptions, and exclusion of some factors, it is expected that a model’s 

performance will be capped at a certain level. The deficiency will remain the same if no further 

effort is made. For example, ordinary least squares can be applied in linear regression to 

minimize the sum of the squares of the differences between the observed dependent variable in 

the given dataset and those predicted by the linear function. The calibrated coefficients can help 

account for only a certain amount of variation in the dependent variables (e.g., in terms of R2). 

Likewise, in the context of incident detection, existing models often cannot achieve a perfect 

detection result. The inherent limitations of a deployed model may not be able to be addressed 

simply through recalibration. 

Instead of tweaking the model again, other approaches that involve revisiting the modeling 

results and gathering related feedback or ensembling other processes to learn the failures may be 

more valuable. The needs of these approaches motivated the research team to expand the 

detection capability through the use of AI that does not limit itself to the fixed-model framework. 

In other words, AI is leveraged to imitate human behavior that may not be perfectly generalized 

by a mathematical model. In practice, operators learn from historical operations that they

misclassified. The gradually accumulated lessons are likely to enable the operator to avoid 

repeating similar mistakes. Instead of purely relying on a model, operators can refer to lessons 

learned or knowledge acquired to double-check the model result. More importantly, operators

can keep updating their knowledge while new cases present. Similar evolvement ability to digest 

and learn things has been frequently used in the literature to build various expert systems, which 

make machines behave intelligently. A well-known example is the IBM Watson question–

answer computer system that successfully won the quiz show Jeopardy! against human 

champions in 2011 (Ferrucci et al. 2013). Watson maintains information from millions of 

documents such as dictionaries and encyclopedias to build its knowledge (Brynjolfsson and 

McAfee 2012; Ferrucci et al. 2013). 

Inspired by the learning and reasoning ability of expert systems like Watson, this project expands 

current incident-detection modeling practices by framing an AI approach that combines a 

memory unit with a tuned model. Figure 3 presents the conceptual architecture of the AI 

approach. In a nutshell, whenever the deployed model (e.g., M1) predicts the occurrence of an

incident, the input data are stored to the memory unit and linked with a label as either a correct or 

false prediction after verification of the event. For example, the first time the model makes a 

false alarm Rti, the input variables Xti will be included in the memory (i.e., knowledge database) 

for future comparisons. At later steps, when false alarms Rtj and Rtk are prompted, their 

corresponding traffic conditions Xtj and Xtk will be further assessed with reference to the 
12 

memory. Because traffic conditions Xtj and Xtk are similar to Xti (which was associated with a 

false alarm), the initial predictions of Rtj and Rtk will be corrected to R̂tj and R̂tk. Thus, despite the 

false prediction of detection model M1, these two later cases will be labeled as nonincident 

scenarios, and their relevant information will be further updated in the memory. Referencing the 

memory at each step can be considered retrieving knowledge. Updating the memory dynamically 

is comparable to a TMC operator evolutionarily accumulating the knowledge. 

Source: FHWA. 

Figure 3. Illustration. An AI modeling framework with memory units and learning ability.

The memory unit described in figure 3 consists of a historical knowledge database that archives

different types of traffic information. This is similar to a dictionary that depicts different traffic 

profiles with relevant indexes to incident occurrence (e.g., true/false alarms in incident

detection). The construction of the traffic profiles in the memory unit may differ depending on 

the data source. In terms of the typical data from loop detectors, this project gathered traffic 

flow, speed, and occupancy, either by station-level averages or lane-by-lane measurements from 

simulation models. Other derived metrics, such as speed/flow variances and correlations, can 

also be included. These multidimensional measurements together represent a snapshot of the 

traffic condition at a given time period. Figure 4 illustrates an example of the memory unit that 

stores the traffic profiles. When model M1 predicts the occurrence of an incident Rtx = 1, the 

prevailing traffic condition Xtx is compared with the memorized information. If similar traffic 

profiles are detected, their indexed incident facts (i.e., absence or presence of an incident) will be 

used to correct the initial prediction from M1. For example, if K traffic profiles in the memory 

unit are very similar to Xtx, the corresponding incident facts I1, I2, ... IK ε {0 = no incident;

1 = incident} will be evaluated. If the incident facts show more “0’s”, the early prediction by M1

is highly likely to be wrong. Thus, the prediction results Rtx = 1 will be updated as R̂tx = 0. If the 

incident facts show more “1’s”, the prediction by M1 is likely to be correct, and the prediction 

results Rtx = 1 will be further confirmed as R̂tx = 1. This process ensures that the model results are

not the sole determinant in the decision and helps reduce the risk of false predictions. Upon 

verification of R̂tx, the corresponding Xtx will also be included in the memory unit for future 

reference.
13 

Source: FHWA. 

Figure 4. Illustration. Memory unit with the archive of historical information for reference.
14 

As discussed, a key function of the memory unit is to facilitate the assessment of the current 

traffic profile. It is used to determine similar historical traffic profiles in each subunit. The 

similarity between the current traffic profile and the historical ones can be determined using a 

number of approaches, such as random forest, SVM, and K-nearest neighbors (KNN). This 

project used the simplest KNN approach to illustrate how the similarity between traffic profiles 

can be quantified. For example, if a traffic profile is defined as Xt(Q,S,O) (where Q represents 

traffic flow, S denotes sensor speed, and O is the occupancy), the similarity can be calculated 

based on the Euclidean distance Dix between two profiles, as shown in equation 2:

(2) 

Where: 

Xtx˗h(Q,S,O) = the current traffic profile Xtx(Q,S,O)’s element obtained at time tx − h. 

Xti˗h(Q,S,O) = the element of the ith historical traffic profile Xti(Q,S,O) measured at ti − h.

i = 1, 2, ... n. 

tx = the period that needs incident occurrence prediction. 

ti = the period of ith historical traffic profile Xti(Q,S,O).

h = the number of time steps and h = 0, 1, ... H. 

In equation 2, tx and ti can be but might not be the same period of the day. With all the calculated 

Dix, the top K profiles Xtg (Q,S,O) with minimum distance Dgx (g = 1, 2, ... K; g is the index of 

selected K historical profiles) are selected. The majority rule can be used to update current 

prediction Rtx = 1 by model M1, as shown in equation 3 through equation 5: 

(3) 

(4) 

(5) 

Where: 

NF = the number of profiles that produced false predictions. 

NT = the number of profiles that produced true predictions. 

In these equations, NF + NT = K. In practice, an odd number K (e.g., K = 5) is suggested so that 

equation 5 will not involve the case of NF = NT. The described procedure will expand model M1’s 

capability to keep learning from the historical profiles. Any new profile can be rolled over to the 

memory unit, which keeps the memory unit as updated as possible. The number of historical 

profiles in the memory unit can be limited (e.g., 2,000 records) to reflect the memory capability 

of an operator. If the number of historical profiles exceeds the limit, the latest profiles will be 

kept and the older ones will be phased out so that the memory unit maintains the latest 

information. 
15 

SPECIFICATION OF MODEL M1 WITH NEURAL NETWORKS

The AI modeling framework in figure 3 requires the implementation of a detection model M1. 

Many candidate models can be used. Instead of simple regression approaches and rule-based

methods, models that capture the nonlinear relationship between traffic measurements and 

incident occurrence are preferred. Among the different AI approaches, researchers often choose 

ANN. This project used a simple ANN to demonstrate a specification of M1 to support the 

AI-based detection framework. Other methods, such as SVM, can also be considered. This 

project’s neural network included only three layers: an input layer, a hidden layer, and the output 

layer. Mathematically, the ANN model can be written as in equation 6: 

(6) 

Where: 

Xa

(c) = the cth element in the ath layer.

ωa

(b,c) = the weight parameter that links and Xa

(c)

. 

= the bth element in the output of the (a − 1)th layer. 

βa

(c) = the bias.

a = 1, 2, ... A = the index of a layer in the neural network. 

b = 1, 2, ... B = the element index in the preceding (a − 1)th layer. 

c = 1, 2, ... C = the element index in the following ath layer.

fNN() = the activation function.

In equation 6, in the simplest scenario A = 3. The activation function can be tanh(), maxout(), or 

sigmoid() for the calculation of a specific layer’s output. For incident detection, softmax() was 

used to calculate the probability of incident occurrence as the final output. The input layer will 

have the elements in the current traffic profile Xtx(Q,S,O). Like the example shown in figure 4, 

this profile can include current and multistep historical measurements for flow, speed, and 

occupancy by multiple sensors (e.g., L1 and L2 in figure 1) along the target highway section. 

To start, input X can be written as (Qt

1

,St

1

,Ot

1

,Qt

2

,St

2

,Ot

2

) because traffic metrics such as flow, 

speed, and occupancy have frequently been used in previous literature. Because congestion may

lead to speed variations, an additionally derived metric, CUSUM, was introduced to depict the 

extent of speed variation (Sattayhatewa 1999; Parkany and Xie 2005). CUSUM is a cumulative 

sum control chart used to depict the cumulative sums of the deviations of the observed variables 

and has been successfully applied in other research areas, such as automatic control (Teng and 

Qi 2003; Liu et al. 2008). Thus, this project introduced the derived CUSUM of speed given 

equation 7 and equation 8: 

(7) 

(8) 

  0 1     

  0 1   
16 

Where: 

μ0 = the mean value of speed in given time steps. 

k = slack value. 

HCSi

t = high CUSUM of speed at t of ith sensor. 

LCSi

t = low CUSUM of speed at t of ith sensor.

In equation 7 and equation 8, μ0 is five time steps in this project, k is set at half of the calculated 

standard deviation, i = 1 stands for the upstream sensor, and i = 2 stands for the downstream

sensor in terms of variables in input X. Whenever the speed value was outside the upper or lower 

limit, the variance was calculated and summed. For example, the typical change pattern when an 

incident occurs is a quick reduction in speed followed by the speed remaining stable. Such 

change patterns can be reflected by the calculated high CUSUM of speed and its peak values and 

thus are expected to provide useful information. 

BENCHMARK ALGORITHMS

Two classic algorithms were used as the benchmarks: the ANN model and CA No. 7. The ANN 

model is equivalent to the proposed model without the learning component. As a comparative 

algorithm, CA No. 7 computes three traffic metrics for comparisons based on the occupancy 

measurements from a pair of loop detectors. These metrics are spatial difference in occupancy 

(OccDFt), relative spatial difference of occupancies (OccRDFt), and occupancy values obtained 

from downstream detector (DOcct), as shown in equation 9 through equation 11: 

(9) 

(10) 

(11) 

Where: 

L1 = the upstream sensor.

L2 = the downstream sensor. 

Occt = the measured occupancy at time t. 

The three metrics obtained in equation 9 through equation 11 were compared with predefined 

thresholds Th1, Th2, and Th3, respectively. If OccDFt > Th1, OccRDFt > Th2, and DOcct < Th3, a 

potential incident was detected. Furthermore, if OccRDFt > Th2 for two consecutive steps, an

incident occurrence was reported. The optimum thresholds can be calibrated.

As shown in figure 5, the four states of decisions are as follows: state 0 means that no incident 

exists, state 1 means that a potential incident is detected, state 2 means that the previous potential 

incident has been confirmed, and state 3 means that the detected incident is continuing. The 

decision tree is used to judge states based on the comparison between observed values and 

thresholds. An incident is considered detected for states 2 or 3. 
17 

Source: FHWA. 

Figure 5. Illustration. Decision tree of CA No. 7.

19 

CHAPTER 5. EXPERIMENTS TO TEST THE PROPOSED AI APPROACH

EXPERIMENTAL DESIGN

Ideally, real-world incident and traffic data would be used to train and test the proposed model. 

A large volume of data is necessary to guarantee the effectiveness of an incident-detection 

approach based on ML or AI. Although it is not challenging to obtain real-time traffic data from 

sensor stations on highways (e.g., from Performance Measurement System of the California 

Department of Transportation), publicly available incident data with accurate incident 

information are scarce. In particular, the precise incident occurrence time is often not well 

archived in the database. In addition, the incident frequency of a single segment of road is

relatively low because of the randomness of the events, causing data imbalance issues when 

preparing the training dataset. 

Because of the limitations described in the previous paragraph, to test the performance of the 

proposed AI approach for this project, an experiment was designed using microsimulation in 

order to have full control over the data collection procedure. The simulation scenarios were 

fine-tuned to reflect the traffic conditions of a typical highway section. Figure 1 illustrates the 

designed test scenarios. A four-lane 4-mi straight highway section with a 65-mph speed limit 

was coded in PTV VISSIMTM. Two sensor stations, L1 and L2, with a distance of DL12 were

placed in the middle portion of the highway section. This project considered three levels for 

DL12: 0.3, 0.5, and 1.0 mi. Each sensor station had four detectors to collect data on traffic flow, 

speed, and occupancy of each lane at 30-s time intervals. An incident was simulated to occur at 

time t between the two sensor stations, with a distance of IL1 to the upstream sensor station L1. 

Three values were considered for IL1: IL1 ≈ 0, representing the incident occurring immediately

after passing sensor station L1; IL1 = 1⁄2DL12, representing the incident occurring in the middle of 

the segment; and IL1 ≈ DL12, representing the incident occurring immediately before reaching 

sensor station L2. The incident was simulated to last 20 min with either one or two lanes on the 

curbside blocked. To simulate incidents occurring during different traffic conditions, 30 levels of 

traffic demand were considered: traffic demand varied from 5,100 to 8,000 vehicles per hour 

(vph) in increments of 100. Altogether, 540 experimental scenarios were created: 3 levels (DL12) 

× 2 levels (lane closure) × 3 levels (IL1) × 30 levels (demand) = 540. Each simulation scenario 

ran for 30 min, with the first 5 min as the warmup period. The incident occurred at t = 10 min. 

Data were collected between t = 5 min and t = 15 min, which allowed data to be gathered for 

5 min before and after the incident. The incident-detection approach was continuously 

implemented during these 10 min. Each scenario was replicated with 10 different random seeds 

in simulations; half were used for training the model and half were used for testing its detection 

performance. The final data included the averaged measurements at the sensor station and the 

lane-by-lane measurements of each detector. 

The simulated scenarios are shown in figure 6. Speed-reduction zones and signal lights were

deployed to simulate incident scenarios. For example, the speed limit under normal traffic 

conditions was set at 65 mph. When the incident occurs, one or two lanes are blocked, and the 

other lanes will also reduce speed through the use of speed-reduction zones. Vehicles will pass 

through the speed-reduction zone with a speed limit of 35 mph. Thus, vehicles in the blocked 

lanes have a chance to change lanes to avoid being stopped near the incident location. The ratio 
20 

of truck traffic to passenger vehicles was set at 1:9. Before incident occurrence, trucks were 

assumed to not be allowed to use the leftmost lane. After incident occurrence, trucks could go 

through each available lane.

Source: Screenshot created by FHWA using VISSIM software by PTV Group®. VISSIM is the intellectual property 

of PTV Group and is used herein under the license purchased by Old Dominion University. 

Figure 6. Illustration. Simulation in VISSIM. 

PERFORMANCE CRITERIA

To evaluate the performance of incident-detection approaches, four measurements that construct 

the confusion matrix were considered: true negative: when there is no incident, an algorithm 

predicts no incident (figure 7-A); false positive (FP): when there is no incident, an algorithm 

predicts incident occurrence (figure 7-B and figure 7-D); false negative (FN): when there is an 

incident, an algorithm predicts no incident (figure 7-C); and true positive (TP): when there is an 

incident, an algorithm predicts its occurrence (figure 7-E). The dotted lines denote predicted 

incident conditions, and the solid lines denote actual incident conditions. In practice, the two 

cases involving FP predictions are critical because these false alarms will incorrectly report the 

situations that need incident clean efforts. Dispatching resources (e.g., responders) to these false 

alarms will greatly increase the incident-management cost. Thus, the incident-detection 

algorithm that produces fewer FPs is preferred. In addition, the number of FNs should be as 

small as possible so that the algorithm will not miss many actual incidents. Also, a good 

algorithm should detect an actual incident as early as possible. Thus, the time to detect (TTD) the 

actual incident was also considered. TTD is defined as the time difference between the actual 

incident occurrence time (tincident) and the reported occurrence time by the algorithm (tdetect), as 

shown in equation 12: 

(12) 
21 

Source: FHWA. 

Figure 7. Graphs. Possible prediction results. 

For comparisons, incident-detection algorithms were implemented in the study period 

(i.e., 10 min in each simulated scenario). If an algorithm reported the occurrence of an incident 

(either TP or FP), it was terminated for evaluation in the remaining period. Otherwise, it was run 

until the end of the study period. This allowed all algorithms under evaluation to have the same 

time horizon and fair testing scenarios. Besides the TTD for each tested scenario, the project 

team calculated two frequently used indicators to quantify the overall performance of compared 

incident-detection algorithms: the detection rate (DR) and false alarm rate (FAR), as shown in 

equation 13 and equation 14: 

(13) 

(14) 

DR is the ratio of the total number of correctly detected incidents (TPs) to the total number of 

actual incidents. This indicator reflects the algorithm’s accuracy at detecting actual incidents. A 

larger value of DR suggests that the algorithm is capable of reporting more incidents after their 

occurrence than algorithms with a smaller value of DR. FAR is the ratio of the total number of 

false alarm cases (FPs) to the total number of tested cases. Algorithms with smaller FARs are

preferred because of their lower number of false alarms. These performance indicators are 

reported based on each level of sensor spacing (DL12). In this project, the total number of tested 

cases at a given level of DL12 is as shown in equation 15: 

(15) 
22 

Because the experiment did not simulate incident-free scenarios, the total number of actual 

incidents is the same as the total number of tested cases at a given level of DL12. If some 

simulation scenarios without incidents were added, these two numbers would be different. The 

average TTD for each scenario running with five random seeds was calculated, and the overall 

average TTD under different flow conditions at a given level of DL12 was also computed.
23 

CHAPTER 6. RESULTS

The designed experiments were implemented in VISSIM to collect the raw data. The output data 

from the loop detectors as well as the derived traffic metrics were organized to meet the input 

need of each incident-detection model. This project used the R program to develop the script for 

testing models with collected loop detector data. As mentioned in chapter 5, 50 percent of the 

data were used to train each model, and the other 50 percent were used to test the tuned model. 

For CA No. 7, the combination of three threshold parameters was numerically searched within 

the range of [0,1] with an interval of 0.05. However, because the optimal FAR and TTD cannot 

be guaranteed with one combination of threshold parameters at the same time, the research team 

selected two submodels of CA No. 7. Specifically, the model CA No. 7-1 (M1) was tuned with 

the aim of minimizing FAR and the model CA No. 7-2 (M2) was tuned with the aim of 

minimizing TTD. Additional selection criteria, such as the total number of false alarms must be 

less than 150 and TTD cannot exceed 180 s, were applied. For comparisons, this project also 

included the classic ANN model (M3) built using averaged sensor data and the AI-Avg. model 

(M4), which combines the aforementioned memory unit and tuned ANN model. Both M3 and 

M4 used one hidden layer with 20 neurons. Finally, the AI-Lane model (M5) with lane-based 

detector data used one hidden layer with 40 neurons. M3, M4, and M5 were also tuned by 

following the necessary calibration steps. All five models were trained and tested with data 

aggregated in 30-s time intervals.

The test results are shown in table 2. Overall, the proposed AI-based approaches that used either 

lane-based detector data (AI-Lane) or averaged sensor data (AI-Avg.) outperformed the 

CA No. 7-1 and CA No. 7-2 models in terms of DR, FAR, and TTD. Although the TTDs of the 

AI-based approaches were similar to those of the ANN model, the AI-based models had higher 

DRs and lower FARs compared with the ANN model. These results suggest that there are 

benefits to including the learning structure in the proposed detection framework because it can 

help correct some false alarms from the initial neural network algorithm.
24 

Table 2. Average predictive performance without specifying incident locations and traffic 

demands.

Lane 

Closure Method

DR (%) FAR (%) TTD (s)

DL12 = 

0.3 mi 

DL12 = 

0.5 mi 

DL12 = 

1.0 mi 

DL12 = 

0.3 mi 

DL12 = 

0.5 mi 

DL12 = 

1.0 mi 

DL12 = 

0.3 mi 

DL12 = 

0.5 mi 

DL12 = 

1.0 mi 

One lane CA No.

7-1 

84.0 61.6 46.0 0.7 4.7 0.2 97.5 91.5 127.2 

CA No.

7-2 

76.2 56.7 42.9 2.9 10.0 0.7 90.8 84.5 130.1 

ANN 99.5 97.3 77.8 0.2 2.0 0.9 66.7 70.2 90.4 

AI-Avg. 99.7 98.4 78.0 0.0 0.9 0.7 66.7 70.2 90.4 

AI-Lane 99.7 98.2 82.4 0.2 0.9 0.0 65.2 83.8 86.3 

Two

lanes 

CA No.

7-1 

90.9 78.7 67.3 0.4 5.5 0.9 90.9 93.6 129.5 

CA No.

7-2 

85.6 78.0 65.8 3.1 8.7 0.4 87.7 92.2 130.0 

ANN 99.3 97.1 93.3 0.4 2.2 0.9 69.6 70.7 95.7 

AI-Avg. 99.8 98.4 93.6 0.0 0.9 0.7 69.7 70.7 95.6 

AI-Lane 98.9 97.8 98.9 0.2 0.7 0.2 67.1 75.5 88.0 

To understand the sensitivity of the proposed approach with respect to traffic volume changes, 

incident locations, and sensor spacing, the project team further investigated TPs, FPs, and TTD 

under each simulated scenario. Figure 8 shows the results from one such scenario, and the results 

of the other scenarios are provided in the appendix. These scenarios include different 

combinations of traffic volumes (from 5,100 to 8,000 vph in increments of 100), sensor gaps 

(0.3, 0.5, and 1 mi), and incident locations (near the upstream sensor, in the middle, and near the 

downstream sensor). Different marks denote the different models (M1 to M5). The performance 

metrics (TPs, FPs, and TTD) were affected by the prevailing traffic flows, sensor spacing, and 

incident locations. In particular, when an incident occurred in the middle of two loop detectors, 

incident detection was challenging for all models. Nevertheless, the proposed AI-Lane model 

was able to capture more incidents with relatively shorter TTD even under lower volume 

conditions compared with ANN, CA No. 7-1, and CA No. 7-2. When the sensor spacing 

increased, each model tended to have more FN predictions. However, among the compared 

algorithms, the proposed AI-Lane and AI-Avg. approaches still produced better results in terms 

of FNs and TTD.
25 

Source: FHWA. 

Figure 8. Graphs. Performance comparison of different models in terms of FPs, TPs, and TTD (0.3 mi, near upstream).

27 

CHAPTER 7. FUTURE WORK

The present study explored the feasibility of using AI approaches to detect incidents. Although 

the proposed approach is promising, the following limitations still exist, so future research is 

needed to improve the overall incident-detection performance.

First, the current study did not use real-world data to validate the AI-based TID approach. Thus, 

assessments with field data are recommended. Field data are prone to issues associated with 

accuracy and missing values, and it is unclear how these issues might affect the TID performance 

of the proposed approach. Thus, it will be meaningful to explore the performance of the 

proposed approach with real-world data. If more simulation studies are performed, calibrating 

simulation models based on field data can help generate more realistic output to support testing 

of the AI-based TID approach. 

In addition, other types of input data can be used to improve TID performance. This project used 

only loop detector measurements such as flow, occupancy, and speed, whereas previous studies 

have introduced contributing factors such as time information and weather conditions to enhance 

TID approaches. For example, taking the traffic flow and time periods into consideration can 

better address the fact that the same incident can lead to different impacts during peak hours and 

off-peak hours. In addition, seasonal effects, events, and holidays also offer valuable 

information. Therefore, exploring suitable ways to incorporate heterogeneous input data into the 

AI-based approach is also important. It is reasonable to expect that more vehicle trajectory 

information in the context of connected vehicles will be available in the near future. Questions 

such as how to use information such as headways, acceleration speed, and lane-change behaviors 

to improve TID performance require more research.

Last but not least, different learning algorithms, especially DL approaches, should be examined 

to improve TID performance and better meet the needs of more scenarios. For example, previous 

studies proved the correlation between traffic states and incident occurrence. Thus, it is possible 

to stack historical traffic information using LSTM or convolutional neural network (CNN) 

approaches to capture such correlations. Nevertheless, TMC operators need to wisely make the

tradeoff between model complexity and TID performance. For example, the AI-based framework 

in the current study is relatively simple and expected to be further improved if complex learning 

algorithms such as CNN and LSTM are incorporated into the framework. But the benefits of 

deep neural network models are accompanied by their high computational complexity and 

difficulties for real-time applications. Therefore, more efforts are needed to assess the suitability 

of various learning algorithms for incident detection.

29 

CHAPTER 8. CONCLUSION

TID is critical for highway operations and safety. A number of automatic TID approaches based 

on loop detector data have been developed in the past, but the performance of these approaches 

is not adequate, which prohibits their widespread implementation. Considering the complexity 

and the nonlinear relationship between traffic incidents and traffic metrics, this project proposed 

an AI-based incident-detection framework that leverages data from loop detectors and typical 

learning features of expert systems. The key components of the proposed framework include the 

ensemble of an ML approach and the memory unit. The use of the memory unit in terms of the 

knowledge database coupled with the similarity analysis between current and historical traffic 

profiles enables corrections to initial incident predictions. The use of the memory unit ultimately 

offers a detection framework with learning and evolving capabilities that benefits from similar 

historical errors. The developed AI-based framework was assessed through a fully controlled 

simulation experiment that consisted of numerous traffic and incident scenarios. The obtained 

results show that the proposed AI-based framework (using either lane-based data or station-level 

average data) performs better than the two classic approaches (CA No. 7 and the ANN). The 

better performance of the proposed approach was primarily demonstrated in terms of shorter 

TTD, lower FAR, and higher DR. The presented analysis results confirm the improved 

performance of the proposed AI-based framework regardless of the sensor spacing. 

The current study emphasizes the architecture of the AI-based framework. This project did not 

focus on assessing whether the involved individual models (i.e., neural network and KNN in this 

study) are superior to other models such as SVM or DL approaches. These integrated algorithms 

can be replaced by other models without changing the proposed framework. For example, if an 

SVM model is adopted as the initial classification algorithm, it can replace the neural network 

component in the framework. Because of the inherent limitations of simulation models, some of 

the simulated data from this project might not perfectly reflect field situations. Given the 

availability of sufficient traffic data and accurate incident records, further testing the 

performance of the AI-based framework with field data in the future is recommended.

31 

APPENDIX. PERFORMANCE COMPARISONS OF DIFFERENT MODELS UNDER DIFFERENT SCENARIOS

Figure 9 through figure 16 describe the performance comparison (FP, TP, and TTD) of five different models (CA No. 7-1, 

CA No. 7-2, ANN, AI-Avg., and AI-Lane) under different conditions (loop detector interval and incident location).

Source: FHWA.

Figure 9. Graphs. Performance comparison of different models in terms of FPs, TPs, and TTD (0.5 mi, near upstream). 
32 

Source: FHWA. 

Figure 10. Graphs. Performance comparison of different models in terms of FPs, TPs, and TTD (1.0 mi, near upstream). 
33 

Source: FHWA.

Figure 11. Graphs. Performance comparison of different models in terms of FPs, TPs, and TTD (0.3 mi, near middle). 
34 

Source: FHWA. 

Figure 12. Graphs. Performance comparison of different models in terms of FPs, TPs, and TTD (0.5 mi, near middle). 
35 

Source: FHWA.

Figure 13. Graphs. Performance comparison of different models in terms of FPs, TPs, and TTD (1.0 mi, near middle). 
36 

Source: FHWA. 

Figure 14. Graphs. Performance comparison of different models in terms of FPs, TPs, and TTD (0.3 mi, near downstream). 
37 

Source: FHWA.

Figure 15. Graphs. Performance comparison of different models in terms of FPs, TPs, and TTD (0.5 mi, near downstream). 
38 

Source: FHWA. 

Figure 16. Graphs. Performance comparison of different models in terms of FPs, TPs, and TTD (1.0 mi, near downstream).
39 

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