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 developed to predict pedestrians’ waiting/crossing intentions with approaching vehicles. Secondly, a Modified Social Force Model (MSFM) is developed by taking the evasion with conflicting road users, the reaction

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 private cars. In the upper level, the carsharing operator determines the vehicle fleet, prices, and relocation operations with the objective of maximizing profits, considering the potential reaction of travelers

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 demanding. To improve the prediction accuracy and achieve a timely performance, the capture of the intrinsically spatio-temporal dependencies and the creation of a parallel model architecture are required. Accordingly

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 insertion heuristic is used to assign trips to a fleet of vehicles and to determine whether engaging in an en-route transfer is advantageous. The operator acts as an endogenous decision maker, updating the

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 traffic flow patterns and a unified algorithm to impute missing values for incomplete traffic flow trajectories. We recommend using subspace-projected functional data clustering with the assumption that each

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 model is established for two urban regions. Instead of the continued feedback control, this paper designs a sampled-data feedback control law to stabilize the traffic accumulations at the desired state.

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 uncertainty of driving behaviors. To address this issue, a novel sequential model, data-driven lane change detection (DLCD) system is proposed using deep learning techniques. Firstly, DLCD system explores

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 record of ridehailing trips in Toronto. Canada. Despite the abundant data, operations and fleet-related data still lacks, and uncertainty still exists concerning actual service provision deployment. Hence, a

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 gives way to traffic behaviours like weak lane discipline, multiple-leader following, etc. Due to these behaviours, queue discharge flow during green time fluctuates, which makes it difficult to identify a

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 trajectories of the entire traffic flow is essential. The current techniques cannot fulfill this need sufficiently. To this end, we developed a hybrid approach for reconstructing vehicle trajectories at signalized

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 reshape the commuting dynamics in the short-run and the implication on AV-related policies in the long run. We find that in the short run, the adoption of AV can create more congestion delays since delay becomes

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 travel

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

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. The PI

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 that quantifies

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 bus driver scheduling problems aim to optimally deploy drivers to fulfil published timetables for bus services subject to drivers’ contractual working rules. In this study, we first develop an integer linear programming model for a practical driver scheduling problem. We proceed to formulate another integer linear programming model for the driver scheduling enhancement to incorporate with uncertain

This study proposes a tensor-based K-Nearest Neighbors (K-NN) method, in which traffic patterns involve multi-dimensional temporal information and bi-directional spatial information. Such multi-temporal information can not only capture the instantaneous fluctuation of short-term traffic but keep the general trend of long-term traffic. In numerical experiments, with taxis’ GPS data from an urban road

It is foreseeable that connected vehicles (CVs) and regular vehicles (RVs) will be operated together on roads in the upcoming decades. The appearance of CVs provides us an opportunity to improve traffic efficiency by guiding drivers to properly steer their CVs. To take advantage of this opportunity, this paper proposes two cooperative driving strategies for CVs that move in heterogeneous RV and CV

Connected vehicles (CVs) are considered to have the potential to significantly improve traffic flow stability. Although several studies have been devoted to modelling car-following behaviour in a connected environment, most model formulations are based on assumptions without empirical observations. Therefore, this paper utilizes data from field experiments to explore the dynamics of CVs. Data mining

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 also carried

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

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,

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

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

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

A motorway weaving section connects a pair of closely spaced entry- and exit-ramps, where intensive lane-changings of merging and diverging vehicles take place over a relatively short space. A detailed trajectory data reveal that a significant proportion of the lane changing at the weaving section exhibits group lane-changing behaviour, in the forms of a lane-changing platoon and simultaneous weaving

A dynamic macroscopic parking pricing model is developed to maximize the revenue for a city, while simultaneously minimizing the total cruising time on the network. The proposed responsive pricing scheme takes the parking search phenomenon into consideration. This means that the parking fee also changes in response to the number of searching vehicles, in addition to changes in response to the parking

This paper addresses the first-order extension of the Lighthill–Whitham– Richards (LWR) macroscopic traffic flow model. Although previous studies have focused on the fluid aspect of traffic flow, none have addressed the sensitivity of drivers to the number of free spaces within a certain distance ahead of the subject driver. To incorporate driver behavior, we used the number of free spaces ahead of