This study uses the Dynamic Spatial Difference-in-Differences model (Dynamic SDID) to analyze the impact of the Taiwan High-Speed Rail (THSR) on Taiwan’s tourism demand. To control for spillover effects, the model incorporates the Taiwan Tourist Shuttle service (TSHU) as an alternative transportation option, the interactive effects between TSHU and THSR, and the spatial autocorrelation between TSHU

This paper investigates the impact of conformity on traveler’s route choice and evaluates the value of conformity in this context. Drawing from classic theoretical model of conformity, we analyze the factors influencing conformity in route choice. Based on this analysis, we develop an integrated choice and latent variable (ICLV) model, incorporating latent variables related to conformity, individual

Utilising the existing infrastructure in railway transit to tackle overcrowding requires more understanding of how people use spaces at stations. This study investigated passenger behaviour while waiting for a train on the platform using the data of the Wi-Fi location tracking systems. The trajectories of 129,354 devices were observed in two weeks at two MRT Circle Line stations in Singapore, which

This paper presents a novel adaptive stated choice experiment to capture range anxiety during long-distance travel. It is assumed that respondents have forward-looking properties allowing them to select from a set of charging alternatives along the route or postpone charging for a later (choice) stage. Data was collected among members of the Danish electric car association. Based on this data, we develop

While previous studies have provided insights into the relationship between weather and ridership, how historical and future weather conditions affect bus travel behavior remains to be addressed. And the differences among advancing, current, and lagging effects, between different traveler profiles are not clear. This research aims to fill the gaps by exploring the effects of historical, current, and

The incorporation of artificial intelligence (AI) and robust optimization methods for the planning and design of relief logistics networks under relief demand–supply uncertainty appears promising for intelligent disaster management (IDM). This research proposes a data-driven hybrid scenario-based robust (SBR) method for a mixed integer second-order cone programming (MISOCP) model that integrates machine

As the oversupply of shipping capacity and the competition within the shipping industry intensifies, liner alliances have emerged as the prevailing mode of cooperation. The members in a liner alliance often have different shipping resources, indicating that they have to coordinate the shipping resources with each other, in order to achieve smooth cooperation. During the coordination, the fairness of

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

This study delves into the potential of dynamic ride-sharing (DRS) systems utilizing the agent-based simulation framework MATSim. Through a comprehensive sensitivity analysis across various scenarios, we investigate the efficacy of a newly developed dynamic ride-sharing extension and unveil key insights. Our findings underscore the pivotal role of user willingness in driving DRS utilization, emphasizing

This study designs a crowdshipping (CS) delivery system with public transport (PT) passengers at the operational decision-making level. In this system, parcel lockers (PLs) are positioned in PT stations, through which small and light parcels are allocated to passengers for delivery to their final delivery addresses (i.e., performing the last-mile delivery). A probabilistic mathematical model is formulated

To address stakeholders’ interests, firms increasingly adopt a dual-purpose agenda, typically involving the pursuit of profits and consumer surplus (CS). This study considers a supply chain dynamic involving a retailer and a national brand (NB) manufacturer, both potentially pursuing dual purposes, to investigate how their dual-purpose nature influences the introduction and quality strategies of the

In group buying (GB), the retailer launches a deal with a discounted product price and a minimum group size requirement. Strategic consumers then determine whether to sign up for the GB deal or purchase the product at the regular price immediately. If GB fails, disappointed GB participants perceive a negative psychological utility and decide whether or not to buy it again at the regular price. Considering

This work is concerned with the daily package shipment problem that aims to find low-cost paths for a large volume of packages and transportation vehicles over a network of transshipment centers (TCs). For Chinese courier companies, this typically involves tens of thousands of origin–destination (OD) pairs and has to be solved in a limited time window every early morning. Inspired by their industry

We study the incentives of an airline and a high-speed rail (HSR) operator to incur sunk costs and cooperate in a hub-and-spoke network with a congested hub airport. Contrary to common wisdom, we find that a high delay cost at the hub reduces incentives to cooperate, and that hub traffic may increase after cooperation. We show that airline-HSR cooperation improves consumer surplus, since higher passenger

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

In high-speed railway systems, unexpected disruptions can result in delays of trains, significantly affecting the quality of service for passengers. Train Timetable Rescheduling (TTR) is a crucial task in the daily operation of high-speed railways to maintain punctuality and efficiency in the face of such unforeseen disruptions. Most existing studies on TTR are based on integer programming (IP) techniques

Truck platooning, a linking technology of trucks on the highway, has gained enormous attention in recent years due to its benefits in energy and operation cost savings. However, most existing studies on truck platooning limit their focus on particular scenarios that each truck can serve only one customer demand and is thus with a specified origin–destination pair, so only routing and time schedules