There can be many vacant private parking spaces near locations of excess parking demand. How can we provide the right incentives to potential suppliers and demanders so that they are shared in the market? We consider a parking-sharing platform in which each agent supplies a parking space and needs another one. We propose a novel parking-sharing mechanism that amends the well-known Vickrey–Clarke–Groves

This paper investigates the electric bus charging facility planning (EB-CFP) problem for a bus transit company operating a heterogeneous electric bus (EB) fleet to provide public transportation services, taking into account uncertainties in both EB travel time and battery degradation. The goal of the EB-CFP problem is to determine the number and type of EB chargers that should be deployed at bus terminals

As the urban rail transit (URT) system plays an increasingly important role in supporting large cities’ mobility around the world, service disruptions have become more prevalent, potentially resulting in severe economic losses and passenger safety issues. It is imperative to investigate effective response strategies to mitigate the effects of such disruptions. In response to URT service disruptions

This paper presents a hierarchical and game-theoretic decision-making strategy for connected and automated vehicles (CAVs). A CAV can receive preview information using vehicle-to-everything (V2X) communication systems, and the optimal short- and long-term trajectory can be planned using this information. Specifically, in this study, the aggressiveness of all preceding vehicles in the car-following

Given a set of geographically dispersed vehicles, the first-mile ride-sharing problem seeks optimal routes to transport customers to a common destination (e.g., a transit station) via shared trips. In this article, we address the trade off between operating costs and service rates. The resulting multi-objective optimization problem is of a combinatorial nature. To obtain Pareto solutions we propose

On-demand transit is a flexible transit service designed to adjust the service schedule and route based on passengers’ dynamic demand. The operation of on-demand transit operates in accordance with physical and socioeconomic environments, and demand patterns. In order to meet the diverse mobility needs in urban areas, integrating different transit services is essential to improve both passenger convenience

Shared space is an innovative, yet controversial concept of traffic regulation that features the coexistence of different types of road users (cars, motorcycles, bicycles, pedestrians, etc.), with the same priority (no traffic lights or signs) and in the same, unsegregated environment. The effect on traffic conditions, as well as on safety, has not yet been systematically documented due to the lack

This paper intends to transform the transportation network equilibrium modeling paradigm via an “end-to-end” framework that directly learns travel choice preferences and the equilibrium state from multi-day link flow observations. The centerpiece of the proposed framework is to use deep neural networks to represent travelers’ route choice preferences and then encapsulate the neural networks in a variational

Max-pressure (MP) control is one of the few traffic signal controllers proven to maximize network throughput or maximally stabilize the network. According to the theoretical results published so far, it can stabilize a network if all intersections are equipped with MP control for all stabilizable demands. However, budget constraints may not allow the installation of MP control on all intersections

Accurate transportation mode identification is essential for traffic management and travel planning. The rapid development of GPS-enabled devices has made it both popular and cost-effective to obtain travel modes from massive GPS trajectory datasets. However, since different transportation modes exhibit significantly different spatial characteristics, varied scale representation can be used to efficiently

The emerging technology of Vehicle-to-Vehicle (V2V) communication over vehicular ad hoc networks promises to improve road safety by allowing vehicles to autonomously warn each other of road hazards. However, research on other transportation information systems has shown that informing only a subset of drivers of road conditions may have a perverse effect of increasing congestion. In the context of

This study develops an autonomous artificial intelligence (AI) agent to detect anomalies in traffic flow time series data, which can learn anomaly patterns from data without supervision, requiring no ground-truth labels for model training or knowledge of a threshold for anomaly definition. Specifically, our model is based on reinforcement learning, where an agent is built by a Long-Short-Term-Memory

Predicting transportation mode choice is a classic challenge of travel behavior research. Over the years, different theoretical concepts and modeling approaches have been applied. This paper elaborates the application of class association rules (CARs) and examines their predictive performance using data extracted from the 2015 National Dutch Travel Survey. To solve the problem how to activate rules

Travel to intercity transportation hubs, such as railway stations and airports, can be the most troublesome and inefficient part of the entire air/railway travel journey, as travelers often carry large luggage and have stringent arrival time requirements. Taking public transportation, such as metro and bus services, is inconvenient to carry luggage and less reliable in arrival time while taking taxi

To improve traffic efficiency at highway on-ramps under heavy traffic, this study proposes a platoon-based cooperative optimal control algorithm for connected autonomous vehicles (CAVs). The proposed algorithm classifies CAVs on both mainline and on-ramp into multiple local platoons (LPs) according to their initial conditions (i.e., spacing and speed), which enables the algorithm to adapt to time-varying

The introduction of autonomous mine trucks can improve the efficiency, productivity, and safety of open-pit mines, but may require more reasonable dynamic truck dispatching system to guide routes and schedules than manual driven mine trucks. In practical operations, for security consideration, only one autonomous mine truck is allowed to pass through each intersection at a time, which may lead to endogenous

Connected and automated vehicles (CAVs) and infrastructure-to-vehicle (I2V) communication have great potential to improve traffic safety and mobility at intersection and road segment levels. However, since transportation is an interconnected network, local transportation state change could lead to broader impacts by rerouting. In this study, we focus on investigating the impacts of en-route locational

The crew planning problem is a key step in the urban rail transit (URT) planning process and has a critical impact on the operational efficiency of a URT line. In general, the crew planning problem consists of two subproblems, crew scheduling and crew rostering, which are usually solved in a sequential manner. Such an approach may, however, lead to a poor-quality crew plan overall. We therefore study

Predicting Estimated Time of Arrival (ETA) for a Multi-Airport System (MAS) is much more challenging than for a single airport system because of complex air route structure, dense air traffic volume and vagaries of traffic conditions in an MAS. In this work, we propose a novel “Bubble” mechanism to accurately predict medium-term ETA for a Multi-Airport System (MAS), in which the prediction of travel

Deep reinforcement learning (RL) has been widely studied in traffic signal control. Despite the promising results that indicate the superiority of deep RL in terms of the quality of solution and optimality over fixed time signal control, the real-world multi-modal traffic flows, especially pedestrians, are not properly considered nor sufficiently investigated. This study presents a novel deep RL-based

The magnitude of the workload associated with the provision of emergency response services in the aftermath of natural disasters, coupled with limited availability of personnel for providing these services, leads to demand–supply imbalances with detrimental effects on the provision of the required services. In this context, personnel routing and scheduling decisions aim to meet the demand as fast as

A Tradable Credit Scheme (TCS) is a demand management policy aiming for more sustainable travel behavior. The regulator defines the total credit cap and the credit distribution; it also determines the credit charges for each travel alternative at different times of the day, which modifies the perceived users’ costs. The credit price is determined by trading of credits between travelers. Defining the

Motivated by food distribution operations for non-profit organizations, we study a variant of the stochastic routing-allocation problem under demand uncertainty, in which one decides the assignment of trucks for demand nodes, the sequence of demand nodes to visit (i.e., truck route), and the allocation of food supply to each demand node. We propose three stochastic mixed-integer programming (SMIP)

Proactive route choice refers to a driver’s taking into account future diversion possibilities enabled by real-time information in a network with random travel times. Route choice experiments were conducted in three types of networks with increasing complexity, where the simplest one has no diversion possibilities and is used to gauge risk attitude. Two apparatuses with different cognitive load, a

In this work we put forward a predictive trajectory planning framework to help autonomous vehicles plan future trajectories. We develop a partially observable Markov decision process (POMDP) to model this sequential decision making problem, and a deep reinforcement learning solution methodology to learn high-quality policies. The POMDP model utilizes driving scenarios, condensed into graphs, as inputs

The continuous growth of air traffic leads to congestion in the surrounding area of airport. The optimization of the air traffic in this area requires the improvement of management strategies, especially for the arrival air traffic. SESAR has proposed the concept of Extended-Arrival MANagement (E-AMAN), which aims to plan the arrival streams from an earlier stage in order to achieve delay absorption

Route choice modeling is a fundamental task in transportation planning and demand forecasting. Classical methods generally adopt the discrete choice model (DCM) framework with linear utility functions and high-level route characteristics. While several recent studies have started to explore the applicability of deep learning for route choice modeling, they are all path-based with relatively simple

In high-density metro lines, frequent disturbances could lead to a domino effect of train delays if no adjustment strategy is imposed timely. In this paper, to enhance train timetable adherence and reduce energy consumption, we provide a hierarchical optimal control framework for the automatic train regulation problem with energy-efficient speed trajectory based on the model predictive control method

Risky and aggressive lane changes on highways reduce capacity and increase the risk of collision. We propose a lane-changing pricing scheme as an effective tool to penalize those maneuvers to reduce congestion as a societal goal while aiming for safe driving conditions. In this paper, we first model driver behavior and their payoffs under a game theory framework and find optimal lane-changing strategies

Advancements in sensor technologies, vehicle automation, communication, and intelligent transportation systems create unforeseen possibilities for the development of novel traffic management approaches in road transport systems. Furthermore, data observations with different accuracy and noise levels are fused towards advanced traffic state estimators. This work builds on the existing family of data

Penetration of connected vehicles and crowdsourced mapping applications give rise to security vulnerabilities in transportation networks. Accurate detection of cyber-attacks on transportation networks is critical to minimize impacts on transportation systems. This task is particularly challenging because the impacts of regional cyber-attacks can be invisible on aggregated traffic data, especially when

Electric buses (E-buses) are increasingly replacing internal combustion powered bus fleets. They can further function as distributed energy storage units. We investigate the feasibility of the novel “Bus-to-Grid” (B2G) or “E-Buses as Power Storage” (EBaPS) concept, which allows battery E-buses to provide transportation as well as power-grid services. By aggregating the electrified bus fleets into Virtual

Vehicle timetabling and scheduling in a public transit system are usually performed separately, with the output of timetabling serving as the input of scheduling. An obvious drawback of this sequential planning method is that the trade-off between bus timetables and vehicle schedules may be neglected when determining solutions, which in turn results in that the obtained solutions may be inferior to

Since the early 2010s, the meal delivery business went through a veritable revolution due to online food delivery platforms. By allowing customers to quickly order from a wide range of restaurants and outsourcing currently available couriers using their vehicles (crowdsourcing), this typology of service dynamically bridges demand and supply. The main goal of online food delivery platforms consists

Self-organized criticality (SOC) is a celebrated paradigm from the 90’s for understanding dynamical systems naturally driven to its critical point, where the power-law dynamics taking place make predictions practically impossible, such as in stock prices, earthquakes, pandemics and many other problems in science related to phase transitions. Shortly thereafter, it was realized that traffic flow might

Parking supply and demand are often imbalanced in urban areas, causing adverse consequences such as excessive search times and long walking distances. Many parking authorities price parking as a demand management strategy by charging either a fixed daily fee or an hourly price for parking. An emerging alternative is progressive pricing, whereby drivers pay an hourly price that increases if their tracked

In this paper we introduce a mesoscopic Train Following Model which accurately captures train interactions and predicts delays based on spacing between consecutive trains. The Train Following Model is applied recursively block by block estimating train trajectories given initial conditions (i.e. the trajectory of an initial train and dispatching headways of following trains from the terminal station)

Due to the tighter budget for pavement management, schedules of inspection activities should be jointly optimized with the maintenance and reconstruction (M&R) plans for pavement systems. Conducting inspections every year is unnecessary and will decrease the budget for M&R activities, while infrequent inspections may lead to suboptimal M&R planning due to the lack of accurate information. This paper

Nowadays, Automated Vehicle (AV) technology is gaining attention as a candidate to improve the efficiency of Bus Rapid Transit (BRT) systems. However, there are still some challenges in AV technology including limited perception range and lack of cooperation capability in mixed traffic situations with drivers. The emerging Connected and Automated Vehicles (CAVs) and Cooperative Intelligent Transportation

The objective of this study is to understand what factors drive household decisions to install electric vehicle charging infrastructure at home. To this end, we administrated an online questionnaire to 1,199 households drawn from across the state of New South Wales in Australia, who were asked to complete one of two discrete choice experiments (DCEs). The DCEs were customized based upon the type of

Longitudinal vehicle control models, such as Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), have been the core component of many Automated Driving Systems (ADS) or Advanced Driver Assistance System (ADAS). ACC and CACC systems make vehicles drive with faster reaction time and smaller following distance than manual vehicles, alleviating the congestions and attenuating

Congestion pricing has long been considered an efficient tool for tackling road traffic congestion, but tolls are generally unpopular. Interest is growing in tradable permits as an alternative. Tolls and tradable permits are interchangeable if travel conditions are unchanging, but not if conditions vary and tolls and permit quantities are inflexible and cannot be adapted to current conditions. We compare

An agent-based model (ABM) simulates actions and interactions of the synthetic agents to understand the system-level behaviour. The synthetic population, the key input to ABM, mimics the distribution of the individual-level attributes in the actual population. Since individual-level attributes of the entire population are unavailable, small-scale samples are generally used for population synthesis

Many recent experiments have shown that the current commercially available adaptive cruise control (ACC) systems demonstrate string instability phenomena. The lower-level time-lag, the sensor time-delay, and the input disturbance primarily contribute to such instability. This paper presents a unified strategy to design ACC systems compensating the lower-level time-lag, the sensor time-delay, and the

The development of technologies for reliable tracking of pedestrian trajectories in public spaces has recently enabled collecting large data sets and real-time information about the usage of urban space and indoor facilities by human crowds. Such an information, nevertheless, may be properly used only with the aid of theoretical and computational tools to assess the state of the crowd. As shown in

Flexible buses provide on-demand services to one or more local communities in a specific geographical area. Bus routes can be adjusted dynamically according to real-time passenger demand in a cost-effective manner. This study investigated the dynamic bus-routing problem considering stochastic future passenger demand. A two-stage stochastic programming model was formulated to minimise the total vehicle

This paper presents additional results of the generalized bathtub model for urban networks, including a simpler derivation and exact solutions for uniformly distributed trip lengths. It is shown that in steady state this trip-based model is equivalent to the more parsimonious accumulation-based model, and that the trip-length distribution has merely a transient effect on traffic dynamics, which converge

This paper proposes an innovative distributed longitudinal control strategy for connected automated vehicles (CAVs) in the mixed traffic environment of CAV and human-driven vehicles (HDVs), incorporating high-dimensional platoon information. For mixed traffic, the traditional CAV control method focuses on microscopic trajectory information, which may not be efficient in handling the HDV stochasticity

This paper leverages emerging highway vehicle platooning technology to improve the efficiency and effectiveness of fleeting trains at minimum headways under moving blocks. The research aims to better understand how closely following trains respond to different throttle and brake control algorithms, and, using insights gained from automobile and truck platooning technology, develop improved train control

The Air Traffic Management system is evolving to deal with efficiency, capacity, safety and environmental challenges. Progress along these fronts requires the development of trajectory planning and prediction tools that can go beyond the current deterministic planning paradigm to deal with an uncertain meteorological and operational context. In this work, we introduce a novel flight planning methodology

Intelligent bus platoon effectively meets the high demand for public transport during rush hours. A decentralized motion planning method based on hierarchical optimization framework is proposed for intelligent bus platoon to improve flexibility and efficiency. This method realizes the optimization of the guidance trajectory in the higher layer, and the optimization of the motion trajectory in the lower

We define a pickup and delivery routing problem with time windows that arises in last-mile delivery. A customer can be served either directly at home, by one of the available capacitated trucks, or via lockers, that allow a self-service option. On the same route, the couriers must deliver the parcels and collect the packages that the customers intend to return. The returned parcels can be picked up

Ride-sourcing drivers enjoy flexibility in scheduling work hours and choosing platforms, which generates multi-homing behavior on multiple platforms. It is challenging to observe the labor supply of multi-homing ride-sourcing drivers due to data limitations, which motivates our research on modeling drivers' dynamic decisions on labor supply in the competitive ride-sourcing market with multiple platforms

Automated vehicles can increase network capacity by leveraging connectivity with other vehicles and the infrastructure, but come at a larger operating and maintenance cost than other human-driven vehicles. This study investigates the trade-off in cost and capacity effects of automated vehicles when a social planner decides the optimal fleet mix for serving a given network demand. We present a system

Underwater radiated noise (URN) emanating from ships can adversely impact the life functions of certain marine mammals that rely on sound to navigate, communicate, and locate prey. This paper formulates an optimal voyage scheduling problem to mitigate the impact of URN on sensitive marine species by choosing amongst different possible paths and specifying the cruising speed along the selected path

Traffic flow prediction is an essential component in intelligent transportation systems. Recently, there has been a notable trend in applying machine learning models, especially deep learning, for network-wide traffic prediction. However, existing studies have limitations on model interpretability, model generalization, and over-reliance on image data processing or fine-designed deep learning structures

Accurate train delay prediction is vital for effective railway traffic planning and management as well as for providing satisfactory passenger service quality. Despite significant advances in data-driven train delay predictions, it lacks of a systematic review of studies and unified modelling development framework. The paper reviews existing studies with an explicit focus on synthesizing a structural

Traffic surveillance cameras are the eyes of the Intelligent Transportation Systems (ITS). However, they are currently isolated and can only extract information from each of their fixed views. To track vehicles across multiple cameras and help public agencies collect link travel time and speed information, an Edge-empowered Cooperative Multi-camera Sensing (ECoMS) System is proposed. ECoMS system presents

This paper presents an adaptive optimization framework for dynamic rail transit network operations with use of a rollout surrogate-approximate dynamic programming method. The optimization algorithm derives coordinated decisions of service schedules and train unit deployment with respect to prevailing passenger demand. Considering the computational effectiveness needed for real-time applications, a

Incidents and other random factors may create variations to the transportation system and thus result in stochastic road capacity during the travel period. The realized capacity on a given day (i.e., an average value over the travel period) changes from day to day. For instance, existing empirical studies indicate that incident capacity reduction can be approximated as a continuous random variable