Disordered traffic streams are generally characterised by the presence of numerous vehicle classes having different sizes and operational characteristics. Unlike homogeneous traffic flow, smaller vehicles percolate between the bigger ones, invalidating the lane discipline and therefore requiring a different modelling approach. Each vehicle traverses through a series of opportunities created by other

This study aims to model traffic flow under weak lane based heterogonous (mixed) traffic conditions. Unlike homogeneous traffic, when a follower (subject) vehicle in mixed traffic moves closer to its leader vehicle, it tends to adjust its longitudinal movement or change its lane and acts discretely. Due to this phenomenon, traffic flow modeling under such conditions is always challenging. A new driver

The estimation of citywide passenger demand plays a vital role in system planning, operation, and management of the urban transit system. The Wi-Fi probe data, one of the emerging crowdsourcing data, is utilized to collect traces of smartphone users in this study. We establish a framework for OD matrix reconstruction, including extracting features for transit patronage and distinguishing them from

Lane-changing is an important driving behaviour and unreasonable lane changes can potentially result in traffic accidents. Currently, the lane-changing data are often recorded with high resolution, which are not appropriate for some common deep learning approaches. To capture the stochastic time series of high-resolution lane-changing behaviour, this study introduces a temporal convolutional network

Station-based one-way carsharing system brings transformation to public mobility and spurs the growth of sharing economy. The accurate estimation of rental and return demand of carsharing stations to support vehicle relocation is essential, hence a station-level short-term demand forecasting method named as station-embedding-based hybrid neural network (SEHNN) integrated by variational graph auto-encoder

Cooperative Automated Driving System (ADS)-equipped Vehicles (CoAVs) can be a promising solution to traffic congestion. We present a reinforcement learning strategy using Genetic Fuzzy Systems (GFS) for cooperative merge of vehicles onto a highway in high density, mixed-autonomy traffic conditions. The CoAVs are trained to make their own decisions purely based on information available from its surrounding

Dynamic left turning using lanes exiting an intersection in the opposite direction is a new type of left-turn traffic organization. When the left-turn traffic volume at an intersection is large, several exiting lanes adjacent to the original left-turn lanes can be converted into left-turn lanes during a certain period of free time under the control of a presignal. Based on kinematic wave theory, this

This paper studies the impact of route choice set of networks on the day-to-day traffic dynamics through conducting a group of experiments, in which four traffic networks with different sets for choice are used. It is observed that a deterministic user equilibrium (DUE) state is difficult to sustain and the probability of reaching DUE is larger in a network with smaller route choice set. Three day-to-day

As travel demand grows in many cities around the world, overcrowding in public transport systems has become a major issue and has many negative effects for both users and operators. Measures to address on-board congestion span from large-scale strategic investments (e.g. increasing infrastructure capacity), through tactical planning (e.g. stopping pattern) to real-time operational measures (e.g. information

This paper proposes a framework of signalized corridor management with vehicle platooning and trajectory control (SCoPTO) using connected automated vehicle (CAV) technologies. In this framework, the major road platoons can request green time extensions when arriving at the downstream intersections to reduce unnecessary stops. The vehicular trajectories of CAVs can also be optimized to maximize the

This paper focuses on adjusting the offline-planned schedules for urban rail networks in the case of large passenger demand. We simultaneously reschedule train services, adjust rolling stock plans, and find the best route for passengers in the updated train schedule. The goal is to improve transport performance for passengers and to balance it with operating cost, while respecting operational constraints

Efficient machine learning algorithms capturing both spatial and temporal dependencies offer promising results for many fields including traffic prediction. However, their performance is still limited, particularly in congested areas where the speed on a road varies significantly. This paper exploits spatial (congestion) dependencies in road networks to improve the performance of spatio-temporal deep

This paper analyzes the impact of road pricing for the bottleneck model with endogenous random delay. We extend the widely used scheduling model by integrating trip scheduling, endogenous traffic congestion, travel time variability (TTV), and road pricing into one modeling framework to analyze the role of road pricing in addressing TTV. This paper investigates the departure profiles and examines the

Traffic flow prediction, one of the essential problems in traffic control and guidance systems, is still drawing increasing attention in recent years with the new methods tipped by the success of AI. In this paper, we propose a novel model, namely self-attention generative adversarial networks for time-series prediction (SATP-GAN). The SATP-GAN method is based on self-attention and generative adversarial

Connected and automated vehicles (CAVs) and regular human-piloted vehicles (RVs) will coexist in the near future. Research has indicated the feasibility of using CAVs to stabilize traffic flow and eliminate stop-and-go waves. In light of this, we develop distributed control schemes to stabilize mixed vehicular platoons by using CAV as the leading vehicle. We address several challenges, such as limited

Limited microscopic simulation models focus on pedestrian movements at signalized intersections, and fewer studies still pay attention to their jaywalking. This paper tries to fill this gap by establishing a microscopic simulation model for pedestrians and vehicles, which can include pedestrians’ jaywalking. The acceptable gap theory model and the social force model are modified to develop this model

This study develops day-to-day models to explore how unreliability of public transit service affects the day-to-day evolution of travel choices. We consider two dynamical processes that incorporate transit service unreliability, i.e., travelers' learning and perception updating process (LPUP) and proportional-switch adjustment process (PSAP). The conditions for existence, uniqueness, and stability

This paper explores new methods for regression models to understand the evolution of headway irregularity of public transport services. In particular, this article is structured around an urban bus service context. This is relevant as headway variability has an important effect on travel time reliability. To do so, a Dynamic Panel Model was estimated to account for serial correlation, which is a novel

The increased interest in time use among transport researchers has led to a search for flexible but tractable models of time use, such as Bhat's Multiple Discrete Continuous Extreme Value (MDCEV) model. MDCEV formulations typically model aggregate time allocation into different activity types during a given period, such as the amount of time spent working and shopping in a day. While these applications

This study provides an automated cooperative control method in multilane freeway merging areas within the connected and autonomous vehicle (CAV) technology. First, a lane-change model for a mainline vehicle considering the ramp traffic demand is proposed to determine which vehicles should change lanes from the outer lane to the inner lane of the upstream mainline in the merging area. Second, the time

When multiple equilibria exist, the desired traffic equilibrium, may not be reached through a day-to-day dynamic adjustment process. Han et al. [“Discrete-Time Day-to-Day Dynamic Congestion Pricing Scheme Considering Multiple Equilibria.” Transportation Research Part B: Methodological 104: 1–16.] proposed a day-to-day dynamic pricing scheme charged on all links to ensure the desired traffic equilibrium

One of the primary challenges of re-planning in high-speed urban railways is the randomness of disruptive events. In this study, an integrated disturbance recovery model presented in which short-turn and stop-skip service operations are optimized together to minimize the average of passengers’ waiting times. This study develops a discrete-event simulation model that employs a variable neighborhood

Network topologies are considered to be the fundamental factors that govern the network’s macroscopic fundamental diagram (MFD) and performance. Despite its significance, this domain remains underexplored. This study employed empirical data as evidence to examine the explicit connections between network topologies and the parameters of a specific MFD, in the form of a macroscopic Underwood’s model

Battery electric vehicle (BEV) has been thought as a key factor in decreasing global greenhouse gas emissions and energy conservation. Therefore, recent developments in BEVs have heightened the need for energy consumption prediction. In this paper, an effectively model in virtue of the accurate velocity prediction approach is proposed. Prior to commencing the study, road information and historical

ABSTRACT In this paper, a network-wide anticipatory control (AC) framework, incorporating drivers' route choice behaviour, is proposed. The proposed AC, consisting of two main levels of control and route choice, explicitly accounts for road users' responses to the implemented control. Perimeter control, modelled in an MPC framework for real-time applications, optimizes inter-regional transferring flow

Network topologies are considered to be the fundamental factors that govern the network’s macroscopic fundamental diagram (MFD) and performance. Despite its significance, this domain remains underexplored. This study employed empirical data as evidence to examine the explicit connections between network topologies and the parameters of a specific MFD, in the form of a macroscopic Underwood’s model

Battery electric vehicle (BEV) has been thought as a key factor in decreasing global greenhouse gas emissions and energy conservation. Therefore, recent developments in BEVs have heightened the need for energy consumption prediction. In this paper, an effectively model in virtue of the accurate velocity prediction approach is proposed. Prior to commencing the study, road information and historical

ABSTRACT In this paper, a network-wide anticipatory control (AC) framework, incorporating drivers' route choice behaviour, is proposed. The proposed AC, consisting of two main levels of control and route choice, explicitly accounts for road users' responses to the implemented control. Perimeter control, modelled in an MPC framework for real-time applications, optimizes inter-regional transferring flow

One of the primary challenges of re-planning in high-speed urban railways is the randomness of disruptive events. In this study, an integrated disturbance recovery model presented in which short-turn and stop-skip service operations are optimized together to minimize the average of passengers’ waiting times. This study develops a discrete-event simulation model that employs a variable neighborhood

Travel time and monetary cost are the two important factors influencing travelers’ route choice behavior. Rather than combining them together as a single objective, a bi-objective user equilibrium (BUE) has been recently proposed in which travelers consider the two objectives separately. It has been shown that BUE can explain more possible route choice results in reality. Therefore, regarding the BUE

Sensing, information, and communication technologies have become fundamental components to transportation system management in the connected era. We have been witnessing an increasing number of ins...

Conflicts between crowds are considered to be critical sources of safety incidents, and choosing a side is a common strategy for conflict resolution. Here, circle antipode experiments, which create significant conflicts and symmetric scenarios, were applied for the investigation of side preference behaviors. In the experiments, more participants (around 70%) preferred to reach the destination from

Conflicts between crowds are considered to be critical sources of safety incidents, and choosing a side is a common strategy for conflict resolution. Here, circle antipode experiments, which create significant conflicts and symmetric scenarios, were applied for the investigation of side preference behaviors. In the experiments, more participants (around 70%) preferred to reach the destination from

Bluetooth data sets allow a direct estimation of travel times across sensor pairs; however, the resulting estimations contain noise because of missed detection rate and alternative paths between sensors. Additionally, although Bluetooth data sets allow tracking of vehicles across sensors, they do not provide an exact path (i.e. a sequence of traffic sections). Estimating vehicle paths from Bluetooth

ABSTRACT Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the

Weaving sections may cause massive congestion and accident problems. Connected and automated vehicles (CAVs) are acknowledged to improve traffic safety and efficiency through effective communication and control. To this end, this study proposes a centralized cooperative vehicle trajectory planning framework for SAE Level 4 or 5 automation. Specifically, focusing on the complex movements at weaving

ABSTRACT Incorporation of Human Factors (HF) into the mathematical car-following (CF) models has always been the research hotspot. Ignorance of such inclusion would inevitably hinder us from acquiring a comprehensive understanding of traffic flow phenomena. This paper proposed a novel CF model in order to bridge three existing research gaps: the demand for more inclusion of HF into the Longitudinal

In this paper, pedestrian path prediction at a signalized crosswalk with pedestrian-electric bicycle-vehicle mixed flow is investigated. Firstly, a waiting/crossing decision model (W/CDM) is develo...

A bilevel nonlinear mathematical programing model is formulated to determine the optimal pricing and operator-based relocations in a one-way station-based carsharing system in competition with priv...

Traffic speed forecasting plays an increasingly essential role in successful intelligent transportation systems. However, this still remains a challenging task when the accuracy requirement is dema...

This study extends the two-sided day-to-day learning framework to simulate the performance of a mobility service using modular autonomous vehicles (MAVs) capable of en-route passenger transfers. An...

Patterns of traffic flow trajectories play an essential role in analysing traffic monitoring data in transportation studies. This research presents a data-adaptive clustering approach to explore tr...

This paper investigates the robust perimeter control for two urban regions with sampled-data and input saturation. Based on the macroscopic fundamental diagram, an uncertain network vehicle balance...

Lane-changing detection is one of the most challenging tasks in advanced driver assistance system (ADAS). However, modeling driver's lane-changing process is challenging due to the complexity and u...

This paper proposes a holistic, within-day ridehailing service provision modelling approach, focusing on large-scale services with human-driven fleets. The model is powered by a full, two-year reco...

Saturation flow estimation is a challenge in non-lane based mixed traffic streams due to the presence of mixed vehicle-types, each having different static and dynamic characteristics. Such a mix gi...

Trajectory data provide a rich source of information on spatial and temporal speed fluctuations of individual vehicles. To assemble a complete picture of traffic operations, obtaining vehicle traje...

Recent empirical studies indicated that using autonomous vehicles (AVs) can reduce commuters' value of time. In this context, this paper investigates how variation in value of time for AVs will res...

ABSTRACT Conventional space–time prisms (STPs) represent deterministic travel times but ignore the stochastic nature of travel speeds. To this extent, reliable STPs were developed to take the effect of time uncertainty into account. Although empirical studies showed that link travel times are correlated in the urban road networks and obey non-normal distributions, existing reliable prisms assume that

Focusing on reducing the traffic congestion on an oversaturated urban metro system, this study investigates the collaborative passenger flow control problem for a metro line. By introducing the timetable-oriented space-time network representation, the problem of interest is finally formulated as an integer programming model, in which the objective function aims to minimize the total weighted passenger

The problem of modeling individual decisions repeatedly over time is important to study dynamics in travel behavior, changes in preferences, and adoption of new services or technologies. Although different methods have been proposed to account for dynamics in pure discrete choice contexts, the case where both continuous and discrete decision variables evolve dynamically has not been fully solved. In

ABSTRACT This paper proposes an improved layout for the Displaced Left-turn (DLT) intersection by combining the conventional DLT intersection with protected left-turn phases, based on the safe-crossing requirements of pedestrians and non-motorized vehicles. The delay and stops are weighted to form an integrated performance index (PI) in a real-time vehicle-to-infrastructure communication environment

ABSTRACT Space is one of the most valuable assets in urban environments. Currently, many cities devote a significant amount of space to private, less sustainable modes of transport. Several attempts have been made to recover urban space for other purposes, e.g. pedestrians, low-speed residential areas, public modes of transport, or bike lanes. In this paper, we propose a new analytical methodology

Weather is fundamentally a perception rather than an objective measure. This study uses data from a four-wave travel diary survey and aims to answer two research questions, i.e. 1. How individuals from different socio-demographic groups perceive weather. 2. How an individual’s weather perception affects his/her leisure activity participation decision. A thermal indicator, Universal Thermal Climate

The dynamics of mixed traffic were analysed by examining the driving features of various vehicles based on their car-following behaviours in a test-driving circuit. By applying decision tree analysis to the observed data, we successfully compared the acceleration, velocity difference and distance gap of different vehicles during driving trials and quantitatively extracted the features of these physical

An effective macroscopic lane change (LC) model is required to facilitate active and dynamic lane management to develop cell-based multilane macroscopic traffic models. Existing logistic regression LC models do not undertake model classification of lane change; do not consider performance measures in the validation of field data and ignore movement between lanes during time-varying traffic. Models

Lane changing is one of the basic driving behaviours, which may induce traffic oscillations and incidents. However, it is difficult to well model the lane-changing decision process due to the complex traffic status. To promote the prediction accuracy of lane-changing decisions, this paper presents a probability lane-changing model by taking into account the memory effect. That is, the lane-changing

The one-way carsharing system has been widely used in the carsharing field due to its flexibility. However, one of its main disadvantages is the imbalance between supply and pickup demand. At present, multi-source data are available for the real-time prediction of pickup demand. The multi-source data that are used for this purpose include real-time user application log data, historical order data,

The MDCEV modelling framework has established itself as the preferred method for modelling time allocation, with data very often collected through travel or activity diaries. However, standard implementations fail to recognise the fact that many of these datasets contain information on multiple days for the same individual, with possible correlations and substitution between days. This paper discusses

ABSTRACT We study the dynamics of a class of bi-agent logistics systems consisting of two types of agents interacting on an arbitrary complex network. By approximating the system with simple microscopic models and solving them analytically, we reveal some universal dynamical features and propose applications of such features for system optimisations. Large-scale agent-based numerical simulations are