Abstract This paper investigates time-series correlations between macroscopic travel speed and parking garage occupancy in downtown area, using the real-time parking occupancy data via SFPark.org and travel speed data from HERE Maps for San Francisco downtown areas as a data-driven case study. This study significantly expands recent work on instantaneous correlations by incorporating variables as time

Abstract Typical reinforcement learning (RL) requires a huge amount of data before achieving an acceptable result, and its performance can be rather poor during initial interacting process. Sample inefficiency and cold-start phenomenon of RL limits its feasibility in a range of real-world applications such as traffic signal control (TSC). On the other hand, a large amount of data on TSC can be accumulated

Abstract The calibration of urban traffic microsimulation models is addressed in this paper, focusing on the multivariate distribution of traffic features. This allows to take into account day-to-day variability and possibly complex statistical dependencies, enabling robust validation and the computation of more accurate statistics. Variability in traffic models can be obtained, in principle, by suitably

Abstract Route planning represents a major challenge with a substantial impact on safety, economy, and even climate. An ever-growing urban population caused a significant increase in commuting times, therefore, stressing the prominence of efficient real-time route planning. In essence, the goal is to compute the fastest route to reach the target location in a realistic environment where traffic conditions

Abstract It has been widely shown that pedestrians’ level of frustration grows with the increase of pedestrian delay, and may cause pedestrians to violate the signals. However, for agencies seeking to use multimodal signal performances for signal operations, the pedestrian delay is not always readily available. To tackle this issue, this study proposed a finite mixture modeling method to estimate pedestrian

Abstract In a world in which Intelligent Transport Systems (ITS) are increasingly being installed on highways, the proper calibration of ITS algorithms is emerging as an essential component of such systems. The key to calibration is profound and correct understanding of reactions by drivers to ITS interventions. Speed compliance is a special kind of driver behavior, highly related to resultant road

Abstract For station-based bike-sharing systems, the balance between user demand and bike allocation is critical for the operation. As a basic operational index, the short-term prediction of bike numbers (flow) plays an important role in demand forecasting and rebalancing resources of bike-sharing networks. Many different methods have been proposed for bike forecast in recent years, and the deep learning

Abstract In the scientific literature, the ratio between the total travel cost under a user equilibrium assignment and the total travel cost under a system optimum assignment is typically referred to as the Price of Anarchy (PoA), i.e., the level of inefficiency that can be eliminated by centralized control in routing. Recently, this concept has been attracting renovated attention due to the new opportunities

Abstract Within the context of a shared on-demand transport system, we study the problem of selecting the stopping points from which passengers should walk to their exact destinations (or from their exact origins). We focus on the single-vehicle case that must follow a predefined order of requests, posing the mathematical program, showing that it can be solved in polynomial time and proposing a heuristic

Abstract Taxis spend a large amount of time idle, searching for passengers. The routes vacant taxis should follow in order to minimize their idle times are hard to calculate; they depend on complex effects like passenger demand, traffic conditions, and inter-taxi competition. Here we explore if reinforcement learning (RL) can be used for this purpose. Using real-world data from three major cities,

Abstract Traffic congestion which has been a common problem faced by almost all of the metropolitan cities across the world is caused by non-recurring traffic incidents such as accidents and vehicle breakdowns in many cases. Therefore, accurate prediction of the incident duration has become an essential part of Intelligent Transport Systems (ITS). Although the data-driven deterministic regression models

Abstract Research on driving automation has investigated the use of takeover requests (TORs) to warn drivers about automation failures that require their intervention. Such failures can occur with information in the environment that drivers can use to anticipate them (e.g., system-limit failures) or without such information (e.g., system-malfunction failures). There is a lack of research comparing

Abstract Providing exclusive right of way for trams in road sections is one of management strategies to improve the operation efficiency of urban public transportation systems. However, the availability of lanes for private cars will inevitably be decreased, which results in congestions. In order to find a balance between tram priority and traffic operational efficiency, a new improved-management strategy

Abstract This paper develops a novel anticipatory Variable Speed Limit (VSL) control strategy that incorporates driver behavior based on trajectory data from probe vehicles. In particular, the research examines how the speed limit controls can be coordinated and optimized to reduce lane changing and overall braking, thus to achieve greater traffic throughput, safety, and sustainability. Driver behavior

Abstract Urban expressway, which plays a significant role in medium-and-long distance express travel, influences transport efficiency of an area in a city. The operation efficiency of an urban expressway system can be promoted by on-ramp metering (ORM). Based on ALINEA (asservissement linéaire d'entrée autoroutière) algorithm, this paper proposed an improved ALINEA method with a wavelet neural network

Abstract In nowadays transportation networks, many multimodal itineraries are possible when traveling from an origin to a destination. Due to the plurality of alternatives, the choice of an option is difficult for the user. In this paper, we propose a novel decision support system to help users in that task. Our method ranks itineraries according to users’ preferences and trip profile based on a fuzzy

Abstract Real-time traffic movements at intersections is vital for transportation and traffic engineering. It helps in providing intersection traffic data and optimizing signal control plans. This study attempts to extend the data coverage by developing algorithms to estimate through and left-turn movements in real-time at signalized intersections. This study is the first attempt to estimate short-term

Abstract Developing appropriate modeling and simulation tools with the capability of analyzing traffic patterns, travel demand, and traveling/driving behavior responses at the regional level is important for the evaluation of active traffic management (ATM) and real-time transportation systems management and operations (TSM&O). In this paper, an integrated travel behavior and dynamic traffic assignment

(2021). Safety impacts and benefits of connected and automated vehicles: How real are they? Journal of Intelligent Transportation Systems: Vol. 25, No. 2, pp. 135-138.

Abstract Travel time distribution estimation is fundamentally important for the evaluation of travel time variability and reliability. For urban roads, signal delays are key components of travel time. They are stochastic and differ for vehicular movements due to different signal timings for through, left-turning, and right-turning vehicles. To better assist travelers in making trip decisions under

Abstract Traffic demands at intersections vary across various periods of a day and from day to day. Generally, fixed time traffic signals are designed considering the average traffic flows across multiple days over a predetermined time interval. This approach overlooks the day to day variability in traffic demand, leading to inefficient and unreliable signal control performance. A signal plan should

(2021). Correction. Journal of Intelligent Transportation Systems. Ahead of Print.

Abstract Nowadays, public transport is considered as an alternative to private vehicles to reduce environmental and social problems in developing countries. Hence, assuring a high passenger satisfaction level in the public transport system is an important task for municipalities and governments. However, passenger satisfaction evaluation of public transport is difficult as the opinions of passengers

Abstract Bus signal priority (BSP) is an active traffic management measure to reduce bus travel delay at signalized intersections and to improve the bus service reliability. In this paper, we present a real-time BSP system with a primary focus on its practical implementation. We tackle two inter-related issues of existing priority systems, namely, real-time computation and solution optimality, using

Abstract To alleviate parking problem and offer convenient and efficient service for drivers in parking selection, a smartphone-based parking guidance algorithm and system implementation architecture are proposed. The algorithm describes parking selection as multi-criteria decision making. Dijkstra algorithm is used to suggest the shortest path for the candidate parking lots. Travel cost criteria for

Abstract It is of practical importance for individual vehicles to automatically identify traffic signal light status to facilitate their decision makings for traffic safety performance enhancement and operation efficiency maximization when they are approaching intersections, especially in near-future fully-or semi-autonomous vehicle-penetrated traffic systems. Traditional methods aim to directly extract

Abstract Reactionary delays that propagate from a primary source throughout train journeys are an immediate concern for British railway systems. Complex non-linear interactions between various spatiotemporal variables govern the propagation of these delays which can avalanche throughout railway network causing further severe disruptions. This paper introduces several machine learning (ML) techniques

Abstract To demonstrate the expected performance of an advanced driver assistance system (ADAS) in an intelligent or a highly automated vehicle test approaches should include a combination of simulations, track tests, and road tests. The main objective of our work was to propose a new evaluation approach conducted by an external entity where the vehicle is treated as a black box. This approach allowed

Abstract In this paper, we focus on a bi-objective school bus scheduling optimization problem, which is a subset of vehicle fleet scheduling problems to transport students distributed across a designated area to the relevant schools. The problem being proven as NP-hard in the literature, we propose an algorithm that seamlessly integrates a reinforcement learning approach with a genetic algorithm. Our

Abstract This paper presents a cost-effective strategy to reduce train delays by controlling passenger flow at the station entry. When a scheduled dwell time delay is likely to occur, the strategy reduces the number of passengers entering the platform by slowing down the opening speed of the automated fare collection (AFC) gates. The dwell time of the next train is predicted to enable proactive passenger

Abstract It’s often hard for existing mathematical models to unify and optimize all control variables including phase time, signal phase sequence, offset and signal cycle at the same time. An optimization model for regional green wave coordinated control based on Ring-and-Barrier structure under unsaturated signalized network is proposed in this study. The constraints of signal timing parameters, such

Abstract Risky driving behaviors can cause accidents, which may result in major material and moral damages. Due to the increase in road accidents, it has become an important issue to identify risky driving behaviors and reward people who drive safely. With the development of technology, it is now possible to model driving behavior through advanced sensors integrated into embedded systems. In this study

Mobile devices and Internet-based applications are producing a significant volume of data that may be used to, at least partially, replace some of the hardware necessary to sense traffic systems. H...

This study presents a multi-objective optimization model for the urban electric transit network problem with the aim of simultaneously designing the layout of bus routes, the frequency and the loca...

Abstract Travel time is one of the most important traffic parameters for travelers, traffic managers, planners, and operators alike. Travel time estimation is a significant component of any intelligent transportation systems (ITS) operations. In countries like India, which are at a nascent stage of ITS deployment, one of the main hurdles is the lack of accurate and automated systems for travel time

Abstract We conduct an examination of the preferences of drivers and passengers alike for in-vehicle interactions with a multistudy approach consisting of (1) a targeted literature survey of applications and user interfaces designed to support interactions with in-vehicle controls and systems based on gesture and voice input; (2) a large-scale survey (N = 160 participants) to understand drivers and

Abstract Real-time queue length estimation and prediction provides useful information for proactively managing transportation networks. Queue spillback from off-ramps onto main lanes of freeways is one of the traffic issues caused by vehicular queues that can be efficiently managed using dynamic queue information. In this paper, a case-based reasoning algorithm combined with a Kalman filter is developed

Automated vehicles (AVs, level 4) are coming on the market in forthcoming years, but it is unsure what is the operational capability of these vehicles in the first place. Especially in four-season ...

From individual Connected and Automated vehicles (CAVs) to system-level Smart City applications, technology is playing a more dominant role in research associated with traffic and granular flow mod...

Abstract Connected and Automated Vehicles (CAVs) can potentially improve the performance of the transportation system by reducing human errors. This paper investigates the safety impact of CAVs in a mixed traffic with conventional vehicles at intersections. Analyzing real-world AV crashes in California revealed that rear-end crashes at intersections are the dominant crash type. Therefore, to enhance

Abstract This research investigated the impact of sensor camera misalignment on the quality of the lane keeping assistance, end user experience and driving performance. Testing was performed with 16 participants, both males and females, with an age range from 25 to 35. The Lane Keeping Assist System (LKAS) errors in lateral offset ranged from −0.66 m to 0.66 m and testing was performed on two roads

A cooperative lane-changing (CLC) motion planning algorithm for partially connected and automated environment is proposed in this study. Unlike conventional motion planner whose goal is merely enabling driving maneuver, this proposed algorithm takes one step further in terms of reducing oscillation and shockwave caused by lane change, hence improves transport mobility. The proposed motion planner is

Abstract Given the aging infrastructure and the anticipated growing number of highway work zones in the United States, it is important to investigate methods to improve work zone mobility and safety. Data suggests that inappropriate merge maneuvers are a major contributing factor to highway work zone crashes that often lead to severe congestion and delay. This research proposes a New England Merge

This research, funded by the United States Department of Transportation Federal Highway Administration, is one of the early initiatives to investigate the potential of advanced technologies to impr...

In transportation, LiDAR sensors have been primarily used in surveying and autonomous driving as a major onboard sensing device to detect field objects. Recently, with reduced price and increased demand from real-time and trajectory-level traffic detection, LiDAR technology sees great potential for becoming a mainstream means of infrastructure-based traffic detection other than only being used onboard

(2020). Correction. Journal of Intelligent Transportation Systems. Ahead of Print.

Motivated by military applications, this work considers connected platoons of ground vehicles of potentially different sizes and presents a model-free approach for optimizing the speed of the plato...

The lane closures caused by the work zone at the approaches create a negative impact on the operational effectiveness of the signalized intersections. This paper presents an innovative design for i...

The growth of electronic commerce generates significant demand for the delivery of personal goods. Crowdsourcing applications have the potential to create more flexible alternatives to existing pac...

Ramp metering is a major measure to reduce traffic congestion and prevent traffic breakdown at on-ramp bottlenecks. However, its efficiency is often limited by the excessive traffic demands on eith...

Abstract The rapid advancements in sensor technologies has resulted in the increased use of Automatic Vehicle Location (AVL) systems for traffic data collection. Global Position System (GPS) sensors are the most commonly used AVL system, majorly because of it being a time-tested technology and being relatively cheap. Also, many of the transportation agencies have their vehicles equipped with GPS sensors

This study develops a feedback perimeter control strategy to maximize the throughput of an incident-affected network. The proposed perimeter control strategy is innovative in two aspects. First, th...

Abstract Whereas the development of ADAS seeks to improve driver’s overall performance with a particular focus on traffic safety improvement; with it comes the requirement and opportunity to objectively evaluate the effectiveness of the technology in improving safety and overall road traffic efficiency. This study evaluates the effectiveness of ADAS in improving driver’s risk perception in near-crash

Most traditional metrics compare origin-destination (OD) matrices based on the deviations of individual OD flows and often neglect OD matrix structural information within their formulations. Limite...

Traffic congestion induces significant economic loss each year, and identifying traffic congestion patterns is necessary for better traffic control and management. Floating car data (FCD) provides ...

Road surface friction significantly impacts traffic safety and mobility. A precise road surface friction prediction model can help to alleviate the influence of inclement road conditions on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity. Most related previous studies are laboratory-based methods that are difficult for practical implementation

Abstract The objective of this study is to model the complex behavior of non-lane based heterogeneous traffic which is predominantly occupied by vehicles with varying physical and dynamical characteristics and their staggered car following behavior. This study proposes a new continuum model by considering the properties of two-sided lateral gap in a non-lane based heterogeneous traffic stream. The

The objective of this study is to model the complex behavior of non-lane based heterogeneous traffic which is predominantly occupied by vehicles with varying physical and dynamical characteristics and their staggered car following behavior. This study proposes a new continuum model by considering the properties of two-sided lateral gap in a non-lane based heterogeneous traffic stream. The model is

Abstract Connected automation systems coordinate vehicle communications and automated vehicle control to improve transportation system mobility, safety, and sustainability. Real-world deployment of connected automated vehicle (CAV) applications and related traffic management technologies will require automation of all vehicle and traffic movements, including platooning, lane change, and merge maneuvers

Abstract In this paper, two new adaptive traffic signal control algorithms are proposed based on data from probe vehicles to realize the coordinated signal control of arterial roads. One is an iterative signal control algorithm, and the other is an optimized signal control algorithm. The proportion of vehicles in the nonstop group is selected as the indicator of the traffic state. The value for this