We propose a nonparametric mixed logit model that is estimated using market-level choice share data. The model treats each market as an agent and represents taste heterogeneity through market-specific parameters by solving a multiagent inverse utility maximization problem, addressing the limitations of existing market-level choice models with parametric estimation. A simulation study is conducted to

Mixed transportation of passengers and freights is an effective strategy for reducing environmental pollution and improving the service level of railway systems. This study addresses the problem of robust train composition and carriage arrangement for the mixed transportation of passengers and freights in a high-speed railway (HSR) network. Specifically, a network-based robust optimization (RO) model

This paper studies the relative performance of congestion pricing versus tradable permits for a congested bi-modal transport system with traffic externalities, that affect passengers only, and societal externalities that go beyond the passengers. The point in this case is infection risks, as in the pandemic. We study this relative performance for the case where there are uncertainties on both traffic

The mass rapid transit (MRT) systems play a pivotal role in urban mobility services but are frequently susceptible to various disruptions. Bus bridging service is a widely-applied substitute transit service in response to MRT disruptions, which requires a significant number of buses to transport stranded MRT passengers. In practical applications, these buses may be dispatched from the nearby bus lines

Under dynamic traffic demand conditions, two issues must be addressed when perimeter control is implemented for congested areas of a road network. The first is to avoid intersection spillback at the boundaries and expansion of the congestion, and the second is to improve the output traffic efficiency of the congested areas to quickly relieve traffic congestion. To address these two issues and solve

This paper investigates solution methods for train eco-driving problems that include the classic single-train eco-driving problem, the single-train eco-driving problem with intermediate time-window constraints, and the eco-driving problem for a fleet of trains under the green-wave policy. The latter two problems are particularly relevant in modern, busy railway networks. We start from proposing a relaxed

In a macroscopic assignment model, traffic flows are distributed onto the network by means of a network loading model. The network loading propagates flows along links via a link model and through junctions or intersections via a node model. Most of the travel time delays are caused by queues forming at junctions or intersections, especially in urban networks. Therefore, the efficiency and accuracy

We initiate the study of dynamic traffic assignment for electrical vehicles addressing the specific challenges such as range limitations and the possibility of battery recharge at predefined charging locations. As our main result, we establish the existence of energy-feasible dynamic equilibria within networks using the deterministic queuing model of Vickrey for the flow dynamics on edges.

The bottleneck model has dominated the field on dynamic congestion, as it is tractable and has closed-form solutions. But it has rather specific outcomes that do not occur in most other dynamic models or under static congestion. In the bottleneck model, pricing can remove all travel delays while keeping the generalized price unchanged, making pricing much more beneficial than in other models. Thus

This paper presents a game-theoretical model of road pricing. The model incorporates an endogenized demand and path-choice user-equilibrium with variable user demand and multiple user classes. Different to most of the literature, the proposed model allows to compute in a direct way the optimal tolls, rather than by trial and error of exogenous toll values and tackles the problem of inactive paths that

Conventional intersection management relies on traffic signals to coordinate conflicting traffic flows and distribute right-of-way. However, delays caused by traffic signals remain a major burden on urban transportation systems. Emerging connected automated vehicles (CAVs) are expected to improve the intersection management by coordinating vehicle movements without relying on traffic signals. Hence

We address a multi-class traffic model, for which we computationally assess the ability of mean-field games (MFGs) to yield approximate Nash equilibria for traffic flow games of intractable large finite-players. We introduce ad hoc numerical methodologies, with recourse to techniques such as High-Performance Computing (HPC) and regularization of Loose Generalized Minimal Residual (LGMRES) solvers.

Revealing the inherent relationships among traffic flow characteristics, fundamental diagram has long been regarded as one of the pillars of traffic flow theory since Greenshields seminal work. When it is combined with the law of (mass/vehicle) conservation, dynamic modelling of traffic flow at the macroscopic level such as LWR and others have thrived. This paper shows that fundamental diagram is only

Emerging autonomous vehicle (AV) technology is expected to bring substantive benefits to the transportation systems. This study focuses on the improvement of travel efficiency in transportation networks with AVs. Inspired by the concept of price of anarchy (PoA), we introduce a novel indicator, the benefit of vehicle automation (BVA), to quantify the efficiency benefit brought by AVs. The BVA is defined

There is growing interest in employing non-normal parameter distributions on covariates to account for random taste heterogeneity in multinomial choice models. In this study, we propose a flexible, computationally tractable, structurally simple, and parsimonious-in-specification random coefficients multinomial probit (MNP) model that can accommodate non-normality in the random coefficients. Our proposed

Transportation networks are crucial for social and economic activities but are susceptible to disruptions. Rapid quantification of the impacts of network disruptions can assist in planning recovery efforts. However, gathering timely and comprehensive information for assessing transportation network state is often challenging and not always possible. This study introduces a network assessment strategy

This paper investigates the effects of new airport on a multi-airport system (MAS) with air-rail integration service and heterogeneous passengers. Vertical structure models are proposed to capture the interactions among the stakeholders: passengers, airlines, and rail operator. In the proposed models, passenger heterogeneity is reflected by a continuous value-of-time (VOT) distribution, and the total

This study explores the optimal decisions of airports on charge, capacity, and suggested passengers’ arrival time at the airport under various regulatory regimes (e.g., single- or dual-till regulation). By considering both aeronautical- and non-aeronautical businesses and further incorporating the non-linear relationship between queuing time in the check-in zone and shopping time in the retail zone

This paper investigates the impact of external uncertainty on morning commute behavior when autonomous vehicles (AVs) are introduced and interact with human-driven vehicles (HVs). We adopt the bottleneck model to study the morning commuting dynamics. In this context, we consider two potential benefits for AVs, i.e., value-of-time (VOT) compensation/reduction and capacity enhancement. We develop an

Under regular metro operation conditions, one critical bottleneck to improving metro transportation capacity is fixed-length train compositions. These fixed-length compositions are mandated to not exceed station platform lengths, thereby limiting the potential for increasing capacity to effectively accommodate oversaturated passenger demand. To this end, we focus on a flexible metro operating system

This paper constructs two-stage Nash Bargaining models to examine two types of capacity purchase agreements (CPAs) for regional airlines: one with a local government and one with a network airline. The study compares the impacts of both CPAs on airline profits, consumer surplus, and social welfare. The results show that within a specific bargaining parameter range, both CPAs increase airline profits

Supply chain (SC) has been increasingly challenged by disruption events (DEs), where super DEs (SDEs) comprising a sequence of DEs, e.g., COVID-19, pose significant threats with long-term impacts. To hedge against SDEs, SC viability has been introduced, whose distinctive feature is the ability to adapt the SC structure. Building SC viability via dynamic SC structural adaptation under SDEs, however

Disruptions on the railway network can lead to reduced availability of the railway infrastructure, which requires rolling stock dispatchers to adjust the planning of the rolling stock. In this paper, we develop fast rolling stock rescheduling methods which ensure feasibility with respect to the availability of the railway infrastructure. In particular, we explore the option of performing shunting movements

The basic corridor problem is significant for the study of traffic congestion and travel behavior, paving way to insights into the spatial dynamics of rush-hour traffic flow pattern. In light of the fact that previous studies have yielded only preliminary results, this paper considers both early and late commuters to provide more comprehensive results. By introducing the continuous schedule preference

This paper addresses traffic merging at highway intersections (labeled as OMM intersections) where mainline traffic with multiple lanes and on-ramp traffic converges, which often represent traffic bottlenecks causing severe traffic congestion and safety issues. To do that, we developed a Compliance-constrained Resilient System Optimal Trajectory Planning (CR-SOTP), which is devised as an event-triggered

Intercity multimodal transportation systems hold great promise for enhancing travel, boosting regional economies, and promoting sustainability. However, realizing their full potential hinges on effective cooperation among transport service providers (TSPs), which current practices often lack. This study proposes a novel framework to address this challenge by leveraging price competition through mobility

This study investigates an automotive maritime transportation planning problem, considering roll-on/roll-off (Ro-Ro) shipping and container shipping. Automobiles are distributed from a manufacturer to overseas dealers through maritime transportation. This transportation process involves three key decisions: the choice of maritime transportation mode for the automobiles, the shipping volume of the ships

Improving redundancy is one way of enhancing transportation network resilience by providing travelers with more alternative travel options in case of disastrous events. This paper studies an alternative means of improving network redundancy via retrofitting critical components at the strategical level, which is less constrained by the land use limitation and is less costly compared to building new

We analyse time-varying tolling in the stochastic bottleneck model with price-sensitive demand and uncertain capacity. We find that price sensitivity and its interplay with uncertainty have important implications for the effects of tolling on travel costs, welfare and consumers. We evaluate three fully time-variant tolls and a step toll proposed in previous studies. We also consider a uniform toll

This paper presents a contextual framework for solving the experience-driven traveling salesman problem in last-mile delivery. The objective of the framework is to generate routes similar to historic high-quality ones as classified by operational experts by considering the unstructured and complex features of the last-mile delivery operations. The framework involves learning a transition weight matrix

We develop interactive optimization algorithms for biobjective problems with continuous nondominated frontiers to search for the most preferred solution of a decision maker who is assumed to have an underlying linear or quasiconvex preference function. We progressively acquire preference information from the decision maker through pairwise comparisons of efficient solutions. We keep reducing the search

Optimizing traffic signal control is crucial for improving efficiency in congested urban environments. Current adaptive signal control systems predominantly rely on on-road detectors, which entail significant capital and maintenance costs, thereby hindering widespread implementation. In this paper, a novel connected vehicle (CV)-based adaptive signal control (CVASC) framework is proposed that optimizes

In one-way carsharing systems, striking a balance between vehicle supply and user demand across stations poses considerable operational challenges. While existing research on vehicle relocation, personnel dispatch, and trip pricing have shown effectiveness, they often struggle with the complexities of fluctuating and unpredictable demand and supply patterns in uncertain environments. This paper introduces

In response to increasingly stringent green regulations, ocean carriers are transitioning their emissions reduction strategy from reducing ship speeds to investing in green equipment. However, because carriers face uncertain demand and a competitive freight market, it is challenging for them to determine the timing and size of green equipment investments. Hence, we construct a real options game (ROG)

People repeatedly demand travel, using available vehicles scattered around space. What can justify vehicle withholding (i.e. preventing others from using it, for own future use) from the social welfare perspective? This paper investigates heterogeneity in the potential cost of search for alternative vehicles as such justification. It is shown that travellers whose search cost is substantially higher

The residual queue during a given study period (e.g., peak hour) is an important feature that should be considered when solving a traffic assignment problem under equilibrium for strategic traffic planning. Although studies have focused extensively on static or quasi-dynamic traffic assignment models considering the residual queue, they have failed to capture the situation wherein the equilibrium link

The gap acceptance framework is the theoretical basis for modelling traffic flow at intersections with a priority control. Reliable estimation methods for key gap acceptance parameters are important to more accurately predict key traffic performance measures such as capacity and delay. A notable challenge is that the critical gaps are not directly observable. Currently, the maximum likelihood estimator

This study investigates the impact of walking and e-hailing on the scale economies of on-demand mobility services. An analytical framework is developed to i) explicitly characterize the physical interactions between passengers and vehicles in the matching and pickup processes, and ii) derive the closed-form degree of scale economies (DSE) to quantify scale economies. The general model is then specified

The capacity region (CR) is a key index to characterize a dynamic processing system’s ability to handle incoming demands. It is a multidimensional space when the system has multiple origin–destination pairs where their service rates interact. An urban traffic network is such a system. Traffic congestion appears when its demand approaches or exceeds the upper frontier of its CR. Part I of this study

This paper considers a green vehicle routing problem termed the time-dependent green vehicle routing problem with time windows (TDGVRPTW). The TDGVRPTW is an extension of the green vehicle routing problem with time windows in green logistics. It considers time-dependent vehicle speed and aims to minimize carbon emissions. Since the travel times and carbon emissions between locations depend on the departure

This paper shows that, in a static traffic model with elastic demand, increasing a toll may raise the equilibrium trip rate through a network. The result is obtained in models with horizontal queues and rules about merges and diverges assumed in the Cell Transmission Model for Network Traffic. The paper looks at three networks: (i) a road with a fixed bottleneck at the end; (ii) a road with a “triggerneck”

An urban network’s supply capability is notably impacted by the operational efficiency of its bottlenecks, namely the intersections. An essential and powerful metric for describing a network’s overall supply performance is the capacity region (CR), which represents all potential combinations of capacities for different intersection movements. This study explores the impact of imminent saturation flow

In this article, we investigate the problem of allocating storage space in a container terminal’s yard to transshipment containers. The main decision here concerns the block to which a container is assigned for storage until it is loaded later by another vessel. We propose a setting where some target performance measures are imposed on the discharge operations. In turn, the allocation decisions are

Connected automated vehicles (CAVs) are equipped with sensors, enabling them to scan and analyze their surrounding environment. This capability empowers CAVs to make informed and efficient decisions regarding their motion; however, the limited spatial range and resolution of these sensors present challenges for achieving full autonomy. Cooperative sensing through vehicle-to-vehicle (V2V) and vehic

Shore power (SP) is an effective way to cut carbon emissions at ports by replacing fuel oil for docked ships. The adoption of SP by ships hinges on the onboard transformer setup cost and the cost saving from SP utilization in comparison with fuel oil. The allocation of SP capacity at ports influences the availability of SP-equipped berths and, along with conventional berths, incurs potential service

This paper proposes a Branch-Cut-and-Price (BCP) algorithm to solve an equitable variant of the Dial-a-Ride problem (DARP), namely Equity-Aware DARP (EDARP), a bi-objective optimization problem that simultaneously minimizes the total routing cost and maximizes the Equity-of-Travel (EoT) outcomes for individual passengers. For passengers, EoT is specified as their detour rate, measured by the ratio

This study examines the effects of parking reservation schemes on travel choices and flow distribution in a commuting system comprising regular vehicles (RVs) and shared autonomous vehicles (SAVs), when faced with limited parking spaces. All possible departure patterns under three parking reservation schemes are explored, involving unreserved and reserved RV commuters, as well as SAV commuters. Associated

Strategic Long-Range Transportation Planning (SLRTP) is pivotal in shaping prosperous, sustainable, and resilient urban futures. Existing SLRTP decision support tools predominantly serve forecasting and evaluative functions, leaving a gap in directly recommending optimal planning decisions. To bridge this gap, we propose an Interpretable State-Space Model (ISSM) that considers the dynamic interactions

Accurate forecasting of bus travel time and passenger occupancy with uncertainty is essential for both travelers and transit agencies/operators. However, existing approaches to forecasting bus travel time and passenger occupancy mainly rely on deterministic models, providing only point estimates. In this paper, we develop a Bayesian Markov regime-switching vector autoregressive model to jointly forecast

Entropy maximization is a standard approach to consistently selecting a unique class-specific solution for multi-class traffic assignment. Here, we show the conventional maximum entropy formulation fails to strictly observe the multi-class bi-criteria user equilibrium condition, because a class-specific solution matching the total equilibrium link flow may violate the equilibrium condition. We propose

We consider a problem where customers dynamically request next-day home service, e.g., repair or installments. Unlike attended home delivery, customers cannot select a time window (TW), the service provider assigns a next-day TW to each new customer if the customer can feasibly be inserted in the service route of the next day without violating the TWs of the existing customers. Otherwise, customer

The lack of cycling infrastructure in urban environments hinders the adoption of cycling as a viable mode for commuting, despite the evident benefits of (e-)bikes as sustainable, efficient, and health-promoting transportation modes. Bike network planning is a tedious process, relying on heuristic computational methods that frequently overlook the broader implications of introducing new cycling infrastructure

Many airports have evolved into "airport cities" by expanding their business ventures beyond traditional goods and services to include hotels, convention centers, and shopping complexes. These airport cities, often referred to as airport malls, now directly compete with downtown stores due to their increasingly similar range of products and services. Both air passengers and local residents can choose

When estimating multinomial logit (MNL) models where choices are made from a large set of available alternatives computational benefits can be achieved by estimating a quasi-likelihood function based on a sampled subset of alternatives in combination with ‘McFadden’s correction factor’. In this paper, we theoretically prove that McFadden’s correction factor minimises the expected information loss in

Standard Discrete Choice Models (DCMs) assume that unobserved effects that influence decision-making are independently and identically distributed among individuals. When unobserved effects are spatially correlated, the independence assumption does not hold, leading to biased standard errors and potentially biased parameter estimates. This paper proposes an interpretable Hierarchical Nearest Neighbor

With the advances in electrical technologies (especially the vehicle-to-grid or V2G technologies), electric vehicles (EVs) now can be used as power storage. The latent power storage capacity in EVs can provide additional flexibility to the power system, and thus helps enhance the overall efficiency, stability and reliability of the power grid. With the V2G facility in place, EV users can choose to

Conventional methods to synthesize population use household travel survey (HTS) data. They generate many infeasible attribute values due to sequentially generating sociodemographics and spatial attributes and encounter a low spatial heterogeneity issue due to a low sampling rate of the HTS data. Passively collected mobility (PCM) data (e.g., cellular traces) provides extensive spatial coverage but

Amid growing interest in the integration of drones into maritime logistics, this paper addresses the drone scheduling problem in shore-to-ship delivery (DSP-SSD), which is both significant and challenging. We introduce a mixed-integer programming model with time discretization that incorporates drone-related constraints, moving targets, and the need for multiple drone trips. While commercial solvers

Machine learning is a powerful tool with the potential to improve a choice model's ability to explain consumer behavior. Although the reference-dependent choice model, developed with an emphasis on real decision-making processes, has an advantage over general discrete choice models in terms of explanatory power and interpretability, there is still a lack of consensus on how the reference point should