Abstract Intelligent traffic control and guidance system is an effective way to solve urban traffic congestion, improve road capacity and guarantee drivers' travel safety, while short-term traffic flow prediction is the core of intelligent traffic control and guidance system. To investigate the long-term memory and the dynamic feature of short-time traffic flow time series, a hybrid model was proposed

Abstract In this paper, we propose an estimation algorithm for the shape of the road using trails of leading vehicles via a linear mixed model (LMM) approach. A vehicle trail is essentially the motion trajectory of a vehicle where samples of the historical path are longitudinally collected from the same vehicle at different points in time. Such measurements can be obtained from the fusion system for

Abstract In recent years, large-scale testing has begun for connected and autonomous driving, making it possible to implement the concept of Dynamic Lane Reversal (DLR), which can quickly shift lane directions to reflect instantaneous flow dynamics. DLR is to make full use of road space and avoid waste of road capacity, and potentially alleviate congestion. However, the impact and feasibility of DLR

Abstract The emerging intelligent connected technology provides conditions for obtaining the trajectory of vehicles. To reduce the adverse effects of stop-and-go vehicles in the bottleneck section of expressways, a strategy of coordinated speed control based on trajectory is proposed in a single-lane. Firstly, the trajectories of the connected and autonomous vehicles (CAV) in the downstream vehicle

Abstract Traffic flow prediction is a basic aspect to be considered in transportation management and modeling. Attaining precise information on near and current traffic flows has an extensive range of appliances and it further aids in managing the congestion. Numerous conventional models failed at offering precise prediction results due to “shallow in architecture and hand engineered in features”.

Abstract The primary objective of this study was to develop a trajectory-level weather detection system capable of providing real-time weather information at the road surface level using only a single video camera. Two texture-based features, including histogram of oriented gradient (HOG) and local binary pattern (LBP), were extracted from images and used as classification parameters to train the weather

Abstract The objective of this study is to analyze the possibility of using vehicle sensors to estimate traffic density in future traffic environments. For the analysis, the simulation experiments used in this study consist of three steps to estimate traffic density based on vehicle sensor data. The first step is to develop software module that can generate error of distance measurement in a similar

Abstract An essential activity of driving is making lane changes. Depending on the driver’s motivation, lane changing events may be classified as discretionary or mandatory. Past research has shown that there is a difference in drivers’ risk-taking behavior when making discretionary and mandatory lane changes. A lane changing decision model, based on Fuzzy Inference System (FIS), has been developed

Abstract Greenhouse gas (GHG) emission and excessive fuel consumption have become a pressing issue nowadays. Particularly, CO2 emissions from transportation account for approximately one-quarter of global emissions since 2016. Electric vehicle (EV) is considered an appealing option to address the aforementioned concerns. However, with the growing EV market, issues such as insufficient charging infrastructure

Abstract Radar is a key sensor to achieve a reliable environment perception for advanced driver assistance system and automated driving (ADAS/AD) functions. Reducing the development efforts for ADAS functions and eventually enabling AD functions demands the extension of conventional physical test drives with simulations in virtual test environments. In such a virtual test environment, the physical

Abstract Determining the trajectories of all vehicles on freeways is a challenging yet critical topic as trajectories reflect the characteristics of traffic flow and serve as a good basis for traffic management and control. With the advances of mobile sensing technology, connected and automated vehicles (CAVs) as a new source of probe car can provide high-resolution sampled trajectory data. Furthermore

Abstract Precise travel route identification is the guiding basis of feasible urban traffic planning and modeling. A range of map matching (MM) algorithms have been studied in previous research to integrate coordinate-based GPS data with digital road network information to identify personal travel route. However, these methods significantly depend on the high-density positioning data, which are usually

Abstract This paper aims to evaluate the traffic and environmental performance when implementing scenarios with shared vehicles (SVs), electric vehicles (EVs) and automated vehicles (AVs) using a macro simulation modeling platform. Several scenarios were performed with different penetrations of shared, electric and automated vehicles to study the impact of CO2 and NOx emissions in terms of individual

Abstract The paper develops models for modeling the availability of bikes in the San Francisco Bay Area Bike Share System (BSS) applying machine learning at two levels: network and station. Investigating BSSs at the station-level is the full problem that would provide policymakers, planners, and operators with the needed level of details to make important choices and conclusions. We used Random Forest

Abstract Aggressive behaviors at exits cause conflicts and increase egress times. To deter agents from aggressive behaviors, we first need to know who the aggressive ones are. Therefore, we developed a method for detecting aggressive agents. We focused on only egress simulations with a cellular-automata model in this article since we would like to deeply investigate theoretical characteristics of our

Abstract Accurate prediction of incident duration and response strategies are two imperative aspects of traffic incident management. Past research has applied various types of regression models for predicting incident durations and quantification of associated factors. However, an important methodological aspect is unobserved heterogeneity which may be present due to several unobserved/omitted factors

Abstract Connected and automated vehicles (CAVs) are expected to change the way we travel. Before both the vehicles and infrastructures are fully automated, users of CAVs are required to respond appropriately to any adverse on-road conditions or malfunction that may prevent the autonomous driving system from reliably sustaining the dynamic driving task performance. The objective of this study is to

Abstract New mobility transport systems are expected to be autonomously driven. Even if public interest and a priori acceptability of such systems are high, there is only a few studies with people that already experienced an automated mobility system. This paper presents the results of a questionnaire study after experience the automated vehicle in the Tornado project. 155 users tested the vehicle

Abstract Pedestrian crossing volume is one of the most important variables used to retime and optimize the signal timing plans for traffic delay migration and traffic safety improvement at signalized intersections. However, most of the existing studies only focus on long-term pedestrian volume estimation for planning purposes. To bridge the research gap, this study applied a Bayesian Additive Regression

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 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 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 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 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 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 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