Accurate estimating causal effects of crashes on highway traffic is crucial for mitigating the negative impacts of crashes. Previous studies have built up a series of methods via traditional causal inference theory and machine learning methods to estimate the impacts of crashes. Since the structures and variable dimensions of traditional causal inference models are pre-defined, they can not accommodate

Electric vehicles (EVs) have been highly favoured as a mode of transportation in recent years. EVs offer numerous benefits over traditional fuel-based vehicles, particularly in terms of the environmental impact. Although electric vehicles offer several advantages, there are certain restrictions that limit their usage. One of the significant issues is the uncertainty in their driving range. The driving

The emergence of vehicle modularity technologies provides an opportunity to develop flexible public transport services that allow en-route vehicle capacity adjustments to meet fluctuating demands. Given the largely imbalanced demands for passenger and freight transport in urban areas, the use of modular vehicles in collaborative passenger and freight transport (co-modality) is expected to increase

Unmanned aerial vehicle (UAV)-based commercial services, exemplified by drone delivery, have captured wide interest in tech companies, entrepreneurs, and policymakers. Structured route-based UAV operations have been implemented for traffic management of UAVs in support of commercial delivery services in cities. Yet, its essence, multi-path planning with constraints is not well solved in the existing

Vehicle trajectories can offer the most precise and detailed depiction of traffic flow and serve as a critical component in traffic management and control applications. Various technologies have been applied to reconstruct vehicle trajectories from sparse fixed and mobile detection data. However, existing methods predominantly concentrate on single-lane scenarios and neglect lane-changing (LC) behaviors

Extracting meaningful information from noisy high-dimensional data is attracting increasing attention as richer and higher resolution data is being collected and used for transportation system planning and management purposes. Discovering critical information via effective data representation learning not only helps reduce data dimension, it also enables a deeper understanding of the underlying properties

This study introduces a novel two-stage stochastic programming model tailored for the unique infrastructure of two-story container ports, a design that significantly enhances land productivity and operational efficiency. Our model, addressing uncertainties in vessel arrival times and container loads, incorporates advanced features like innovative quay cranes, rooftop solar panels, and dense container

Robust aircraft maintenance routing problems (RAMRPs) have been extensively studied due to their significance on flight schedule reliability. In the current practice, the traditional huge A-check program is divided into multiple heterogenous small maintenance packages (with different maintenance durations and risk levels) and performed differently during the ground time between two connected flights

To improve the performance of systems, optimization has been the prevailing approach in the past. However, the approach faces challenges when multiple goals shall be simultaneously achieved. For illustration, we study a multi-agent system, where agents have a plurality of different, and mutually inconsistent goals. We then allow agents in the system to vote on which traffic signal controllers, which

This paper aims to incorporate the throttle handle level prediction in high speed train(HST) operation prediction problem to enable the prediction of HST drivers’ activities, in which the key instructions available to HST driver are difficult to determine. Specifically, we consider an end-edge-cloud orchestration system to capture the real-time responses for driver state changes. By adding edge computing

Tradable credit schemes (TCS) have been receiving increasing attention as an alternative to congestion pricing due to considerations of equity and revenue neutrality. Although it is typically assumed that credit transactions occur between travelers directly, i.e., via peer-to-peer (P2P) trading, the underlying mechanism that achieves market clearing (in terms of matching of sellers and buyers and pricing

Merging behaviour is a fundamental yet challenging driving task which has significant impact on traffic flow operations. While numerous efforts have been made on the modelling of decision-making of lane-based merging behaviour, little was focusing on the simulation of the complete merging process, which generates two-dimensional merging trajectories allowing for the investigation of merging behaviour’s

Two-Echelon Vehicle Routing Problems (2E-VRPs) describe the distribution of goods via two echelons of vehicles and sets of transshipment facilities. They have received growing attention in academic research and their applications are becoming increasingly widespread in modern last-mile logistics systems. This paper introduces a new and extensive variant of the 2E-VRP that combines important real-world

This research paper focuses on the optimization of train timetables and maintenance windows, both of which significantly impact service quality and cost-effectiveness. Uncertainties in both elements can disrupt established transportation plans, causing train delays and maintenance cancellations. Accordingly, we highlight the necessity of augmenting the robustness of these schedules. In this study,

Signal control has been effective to alleviate urban traffic congestion. Massive related works about signal timing optimization have been proposed and led to many signal control methods and systems. In recent years, reinforcement learning (RL) algorithms have attracted the increasing attention of researchers in the area of signal control optimization, since they can learn the optimal timing policy

String stability plays a crucial role in regulating traffic flow, as traffic oscillation can be triggered by string instability in the car-following (CF) behavior. Although studies over the past decades have provided various methods for analyzing string stability of analytical CF models, no studies have focused on neural network (NN) based CF models despite the fact that these models have exhibited

We establish a methodology that fuses point data with trajectory data leading to trip chains that reflect whether a person has visited key locations. In our study the point data are Wi-Fi data set from sensors at key tourist locations and the trajectories are a small sample of GPS footprints. The approach proves valuable in the context of tour estimation, placing a specific emphasis on determining

The rise of autonomous driving may sharply reshape ride-sourcing markets by influencing drivers’ income and participation, customers’ choices, ride-sourcing platforms’ operations, and governments’ regulations. We introduce a model that characterizes a ride-sourcing market with a mixed fleet of non-atomic autonomous vehicles (AVs) and atomic human-driven vehicles (HVs). Human drivers decide whether

This paper addresses a new multi-commodity unpaired pickup and delivery vehicle routing problem in which each vehicle is allowed to visit each customer more than once for both pickup and delivery of each commodity. It is a complex variant of the classical capacitated vehicle routing problem and comes from resource transfer among different plants in a distributed manufacturing environment. Notably,

The management of mixed traffic systems is critical to realize the benefits of connected and automated vehicles (CAVs). Generally, the benefits of CAVs can be categorized into the one-dimensional benefits of improving car-following performance and the two-dimensional benefits of efficiently addressing right-of-way conflicts. Researchers have proposed dedicated lanes that can exploit the one-dimensional

The presence of Blockchain Technology (BCT) and its potential supply chain (SC) and logistic benefits—such as improved resilience or lessened disruption—have motivated organizations to consider BCT investment. BCT is a relatively novel technology and requires multiple participants to meet its greatest potential. Its novelty is a barrier to its adoption and can be dependent on an innovator or leader

Multivariate extreme value modelling techniques are widely applied to estimate crash risks from traffic conflicts, with a predominant focus on rear-end crashes. In contrast, the suitability of conflict measures within a multivariate framework for estimating opposing-through crash risks has received less attention. This study proposes a non-stationary bivariate extreme value model to identify a suitable

Major transportation network companies (TNCs) have promised to shift to 100% electric vehicles (EVs) in the next two decades, which places an increasing need to investigate the issues of ride-hailing services provided by EVs. Existing studies that model the EV charging systems and the TNC service systems omit the influences of the charging costs (i.e., electricity rate, and value of waiting time) on

This paper focuses on the discrete choice estimation problem, which involves multiple objectives and testing a broad range of hypotheses that can affect both interpretability and prediction accuracy. Previous studies have proposed mathematical programming formulations to assist with hypothesis testing and estimation. However, there is limited knowledge regarding the effect of in- and out-of-sample

Airspace complexity is defined as an essential indicator to comprehensively measure the safety of air traffic operational situations. A reliable prediction of airspace complexity can provide practical guidance for formulating air traffic management strategies and resource allocation. Although extensive efforts have been devoted to computing airspace complexity, previous studies can rarely model the

Missing values are prevalent in spatio-temporal traffic data, undermining the quality of data-driven analysis. While prior works have demonstrated the promise of tensor completion methods for imputation, their performance remains limited for complicated composite missing patterns. This paper proposes a novel imputation framework combining tensor factorization and rank minimization, which is effective

The burden of first-mile connection to public transit stations is a key barrier that discourages riders from taking public transportation. Public transit agencies typically operate a modest fleet of vehicles to provide first-mile services due to the high operating costs, thus failing to adequately meet the first-mile travel demands, especially during peak hours. At the same time, private cars are underutilized

In this paper we propose an automatic trajectory data reconciliation to correct common errors in vision-based vehicle trajectory data. Given “raw” vehicle detection and tracking information from automatic video processing algorithms, we propose a pipeline including fragments that describe the same object (vehicle), which is formulated as a min-cost network circulation problem of a graph, and to enhance

Noise is inevitable in the collection of traffic data, which may cause accuracy and stability issues in smart mobility applications. In the literature, most of the existing studies on traffic data denoising assume that noises follow specific distributions (e.g., Gaussian) or structures (e.g., sparsity). However, various noises may coexist in the traffic data and their distributions or structures are

The potential lack of realism in stated-preference surveys is particularly acute in contexts where disaggregate real-world data is challenging to obtain. Mobility-on-Demand (MOD) services present one such context. The MOD context is unique due to factors such as service reliability (difference in stated vs. actual travel and waiting time) and current mode inertia which affect the choice of MOD services

Last-mile delivery is regarded as an essential, yet challenging problem in city logistics. One of the most common initiatives, implemented to streamline and support last-mile activities, are satellite depots. These intermediate logistics facilities are used by companies in urban areas to decouple last-mile activities from the rest of the distribution chain. Establishing a business model that considers

Air traffic management of terminal manoeuvring area involves high complexity as air traffic converges to airports. In addition, air traffic is currently experiencing a remarkable growth despite the COVID19 pandemic effects. This trend, which is expected to continue in the mid and near future, motivates the development of methodologies that improve the efficiency and automatisation of air traffic management

This paper proposes a methodology to estimate uncertainties in automated vehicle (AV) dynamics in real time via Bayesian inference. Based on the estimated uncertainty, the method aims to track the car-following (CF) performance of the AV to support strategic actions to maintain desired performance. Our methodology consists of three sequential components: (i) the Stochastic Gradient Langevin Dynamics

Deep learning methods are emerging as popular computational tools for solving forward and inverse problems in traffic flow. In this paper, we study a neural operator framework for learning solutions to first-order macroscopic traffic flow models with applications in estimating traffic densities for urban arterials. In this framework, an operator is trained to map heterogeneous and sparse traffic input

This paper investigates a novel drug distribution system for the epidemic situation by linking two separate models. The improved SEIQR spread model and the multi-period vehicle routing optimization model are integrated to fit the epidemic environment. The epidemic spread model is used to capture virus transmission characteristics and drug demand fluctuations. Given this, we formally describe and model

Driver lane change intention (DLCI) predicting has become an essential research for the development of human–machine co-driving system. This work makes an attempt to predict the DLCI, which is the result of complex interaction between human drivers and driving scene. While few works have explored the relationship between driver behavior features and key features of driving scene when predicting the

This study formulates a latent class-based driving behaviour framework for modelling vehicles’ two-dimensional (2D) movements while considering drivers’ strategic intents and multi-vehicle anticipation (MVA) in heterogeneous, disorderly (HD) traffic conditions. Specifically, five extensions are proposed to a typical stimulus–response based driving behaviour framework. First, the subject vehicle’s 2D

E-scooter sharing is a potential feeder to complement public transit for alleviating the first-and-last-mile problem. This study investigates the integration between shared e-scooters and public transit by conducting a comparative analysis in 124 European cities based on vehicle availability data. Results suggest that the integration ratios of e-scooter sharing in different cities show significant

The timely transportation of goods to customers is an essential component of economic activities. However, heavy-duty diesel trucks used for goods delivery significantly contribute to greenhouse gas (GHG) emissions within many large metropolitan areas, including Los Angeles, New York, and San Francisco. To reduce GHG emissions by facilitating freight electrification, this paper proposes Joint Routing

Accurate traffic prediction is important for efficient traffic operation, management, and user convenience. It enables traffic management authorities to allocate traffic resources efficiently, reducing traffic congestion and minimizing travel time for commuters. With the increase in data sources, traffic prediction methods have shifted from traditional model-based approaches to more data-driven methods

Current advanced driver assistance systems (ADASs) do not consider drivers’ preferences of evasive behavior types and risk levels under rear-end near-crash scenarios, which undermines driver satisfaction, trust, and use of ADASs. Additionally, spatio-temporal interactions between vehicles are not fully involved in current evasive behavior prediction models, and the influence of evasive behavior is

Focusing on the energy-conservation train operation issues, this paper proposes an effective real-time train regulation scheme for metro systems with energy storage devices. Specifically, to minimize train timetable deviation, passenger waiting and energy consumption, we formulate a mixed-integer nonlinear programming model to generate energy-efficient train regulation strategies. This model explicitly

This paper proposes a multi-vehicle cooperative control scheme in mitigating traffic oscillation (MCCS-MTO) for a single-vehicle lane change (LC) scenario applied to connected and autonomous vehicles (CAVs) with guaranteed executing applicability. Specifically, a hierarchical structure is applied in the proposed MCCS-MTO to dampen traffic oscillation on both original and target lanes. It decomposes

Vehicle trajectory reconstruction is an indispensable step before using the observed trajectory data for analysis. A recurrent challenge of the existing smoothing/filtering-based methods is the design of the smoothing parameters to avoid over-smoothing while ensuring realistic vehicle dynamics, but they often fall short in two-dimensional (2D) vehicle movements with coupled longitudinal and lateral

Based on the back-stepping technique, this paper designs an observer-based event-triggered adaptive platooning control algorithm for autonomous vehicles (AVs) with motion uncertainties (e.g., unknown AV mass, internal resistance, and external disturbances). To avoid the transmission of excessive multi-vehicle status information (i.e., speed, position, and so on) between AVs, the adaptive platooning

The disparity between rapid urbanization and limited service supplies has raised significant societal concerns, such as overcrowding, caused by a surfeit of individuals traveling at the same time. However, our understanding of how people decide the timing of their trips remains incomplete. Here we use anonymized smart card transaction data from mass transit railway (MTR) systems across three cities

In the emerging era of autonomous vehicles (AVs), an important question is how to integrate AVs in a multi-modal framework. Recent studies on Transportation Management Centres (TMCs) for future AVs addressed the issue of providing system optimal (SO) solution. However, none of them considered the possibility of developing a multi-modal SO-based solution. In this regard, we propose a novel multi-modal

One of the biggest challenges in the development of learning-driven automated driving technologies remains the handling of uncommon, rare events that may have not been encountered in training. Especially when training a model with real driving data, unusual situations, such as emergency brakings, may be underrepresented, resulting in a model that lacks robustness in rare events. This study focuses

The objective of unlabeled scene adaptive crowd counting (USACC) is to adapt the crowd counting model to a particular scene by utilizing only a handful of unlabeled images from that scene, rather than considering all the diverse scenarios that may occur in the unknown environment at once. The resolution of this problem facilitates the fast widespread deployment of crowd counting models, mitigating

Precise comprehension about the impacts of drivers’ rational and perceptual characteristics on their behavioral decisions is crucial for the accurate prediction of driving behavior. In the previous research on driving behavior, drivers were regarded as homogeneous and absolutely rational individuals. To overcome this limitation, the coupling effects of bounded rational cognition and diverse emotions

Autonomous driving systems must provide safe, predictable, and consistent behaviors across diverse scenarios to enhance user experience. The geometric driver risk field (GDRF) method is proposed for human-like, interpretable, and fast risk estimations. The method models the driver’s subjectively perceived risk with fields formed by sets of geometric shapes. Unlike current safety measures adopted in

Difficulty in finding parking spaces and high parking fees discourage private car usage. Fully autonomous vehicles (AVs) capable of self-parking away from destinations will likely remove this barrier. Despite extensive survey-based research on AVs in recent years, existing literature has not sufficiently addressed the potential impact of new parking options on the demand for these vehicles. This study

The design of synchronized services is of great significance to the development of intermodal transportation. In the planning of Air and High-Speed Railway (HSR) integration services (AHIS), the operators aim to synchronize flight and train schedules and determine the possible transferring options for intermodal passengers. This paper attempts to design an intermodal timetable for train and flight

Staircases play an essential role in crowd dynamics, allowing pedestrians to flow across large multi-level public facilities such as transportation hubs, shopping malls, and office buildings. Achieving a robust quantitative understanding of pedestrian behavior in these facilities is a key societal necessity. What makes this an outstanding scientific challenge is the extreme randomness intrinsic to

This paper presents an internal model-Kalman filtering-based optimal hybrid feedforward/feedback control strategy for traffic platoon control coordination enabled by SAE Automation Level 2 or Level 3 vehicles, i.e., partially automated vehicles (PAVs). Based on the Helly linear car-following model, a PAV platoon is established. Taking each vehicle’s characteristic polynomial as the dominant internal

The rapid expansion in group size of online ride-hailing drivers has made anomalous driver detection become a critical issue, which substantially affects the safety and operation aspects of ride-hailing services Existing studies mainly focus on the identification of abnormal trajectories, while none of them investigate anomalous driver detection. The former evaluates a specific trajectory, while the

To address the battery-related shortcomings of battery electric buses (BEBs), dynamic wireless charging (DWC) technology that allows BEBs to charge while in motion has emerged, thereby extending the driving range and reducing the size of onboard batteries. The introduction of DWC technology raises a critical problem, namely, the deployment of DWC facilities. To resolve the infrastructure planning problem