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This essay provides an introduction to the field of travel forecasting from a historical perspective. Drawing on the book by Boyce and Williams (Forecasting urban travel: past, present and future. Edward Elgar Publishing, Cheltenham, 2015), I first recount the field’s three developmental phases, from inception to maturation. I then summarize significant innovations in model development, organized under

Universities are major trip attractors and generators in large cities, and they have a significant influence on the transport network particularly in high-density areas. The trips to and from university campuses are made by staff, students, and visitors, with an important daily rotation of people (e.g., students who leave early, arrive later, etc.). In this study, we aim to improve our understanding

In car-dominated cities like Melbourne, Australia, limited data on cyclists’ travel patterns and socio-demographic differences complicate understanding of the effectiveness of infrastructure investment interventions aimed at promoting cycling. Recent advancements in city-scale transport modelling enable virtual testing of such interventions. However, the application of agent- and activity-based models

Evaluating spatial access to urban opportunities (such as jobs) has been an emergent approach to studying urban inequities. Unlike in developed countries, where scholars have identified a significant correlation between socioeconomic inequality and residential segregation, Mexico City exhibits relatively high levels of inequality but not extensive segregation in terms of educational outcomes. What

The Covid pandemic from 2020 has affected transport systems worldwide. The British case is examined, drawing on extensive publicly-available data to describe not only impacts on ridership, but also changes in service output, public expenditure, and some indicators of productivity, with particular emphasis on the rail system, and local buses within England outside London. Expectations that the peak

Walking and cycling are widely encouraged to improve safety, promote health and avoid externalities generated by other transport modes, such as air and noise pollution, and greenhouse gas emissions. Many practitioners and policymakers turn to well-established active mobility cultures, such as the Netherlands, to identify best planning practices. However, walking and cycling rates remain low, and arguments

In this study, a new approach will be discussed in which routing is done by predicting future traffic and the learning algorithm is optimized during navigation. Traffic has a complex structure that is constantly changing. Especially for long-term travel, it is not an optimum approach to suggest a route only by considering the traffic situation at the time the navigation request is made. For this reason

Longitudinal studies have become increasingly popular for investigating changes in behaviour, but present additional challenges around participant recruitment, retention, engagement with survey tasks, additional burden and ultimately data quality. Personal technologies, particularly smartphones, have become integral to tackling these challenges but come with their own caveats around user acceptance

The unpredictable and sudden devastation caused by earthquakes demands comprehensive disaster preparation and response operations. In this study, we propose a two-stage robust stochastic optimization model for relief pre-positioning, incorporating post-disaster relief shipment considerations under uncertain parameters that modeled in an ad hoc event-wise uncertainty set, accounting for uncertainties

To alleviate the imbalance between taxi supply and demand at transportation hubs, this paper proposes a double-ended queuing system to explore the taxi-passenger match mechanisms. Based on Markov theory, we construct a queuing model framework that incorporates different information visibility: unobservable (nothing provided), partially observable (expected waiting time provided), and fully observable

We study a retailer’s joint decisions on product assortment planning, pricing, and delivery-time commitments, taking into account the presence of reference effects of delivery time. Specifically, we consider the retailer committing to delivery times for its consumers, and consumers exhibiting reference dependence and loss aversion to the retailer’s delivery times. We characterize the optimal assortment

Warehouses play a crucial role in freight transportation, and their pricing strategies affect warehouse location choices and associated environmental impacts. Although most firms rent storage spaces, limited studies have examined warehouse rental prices (WRP). Furthermore, most studies assume a pre-defined relationship between WRP and its correlates. This study applies spatial machine learning models

Order picking, a labor-intensive and costly aspect of picker-to-parts warehouse systems, faces challenges such as rising worker injuries, faster delivery expectations, and dynamic order arrivals. Collaborative automation, particularly human-autonomous mobile robot (AMR) collaboration, offers a promising solution, but limited research addresses its role in managing dynamic orders. This study introduces

The preemptive facility interdiction problem aims to disable or diminish future threats from adversaries by proactively targeting their critical facilities. This research generalizes a preemptive facility interdiction problem with capacitated facilities. The problem is formulated as a bi-level optimization problem in which the interdictor, at the upper level, decides to attack several facilities to

Manufacturing companies who ship goods globally often rely on external Logistics Service Providers (LSPs) to manage the containerization and transportation of their freight. Those LSPs are usually required to follow rules when deciding how to mix the goods in the containers, which complicates the planning task. In this paper, we study such a freight containerization problem with a specific type of

The proliferation of e-commerce and urbanization has significantly intensified delivery operations in urban areas, boosting the volume and complexity of delivery demand. Data-driven predictive methods, especially those utilizing machine learning techniques, have emerged to handle these complexities in urban delivery demand management problems. One particularly pressing issue that has yet to be sufficiently

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.

Vessel sequencing, speed optimization, and berth allocation comprise the primary interventions for servicing calling vessels in a busy seaport. The objectives are to minimize vessel completion time and reduce carbon emissions, thus balancing port service efficiency with environmental sustainability. Despite interdependent, these challenges have often been addressed in isolation, leading to sub-optimal

National freight transport models are valuable tools for assessing the impact of various policies and investments on achieving decarbonization targets under different future scenarios. However, these models struggle to address several critical elements necessary for strategic planning, such as the development and adoption of new fuel technologies over time, inertia in transport fleets, and uncertainty

Drive-by sensing is a promising data collection paradigm that leverages the mobilities of vehicles to survey urban environments at low costs, contributing to the positive externality of urban transport activities. Focusing on e-hailing services, this paper explores the sensing potential of taxi fleets, by designing a joint matching and pricing scheme based on a double auction process. The matching

The widespread adoption of buy-online-pickup-in-store (BOPS) services has attracted significant research attention, particularly regarding when an online retailer should deploy BOPS service for last-mile delivery (LMD). This study examines the efficiency of BOPS adoption in a competing environment. Using a game-theoretical model within a duopoly framework, we investigate the pricing and BOPS service

We consider a last-mile parcel delivery problem where a vehicle with a companion drone visits a set of ordered neighborhoods, following a line of travel that starts and ends at the depot. The decision-maker restricts the drone to fly within the neighborhood being serviced by the vehicle and seeks to optimize the vehicle and drone operations so that the total time to return to the depot, upon completing

Recently, the rapid development of wireless technologies, low-priced equipment, advances in networking protocols, and access to modern communication, electrical, and sensing technologies have led to the evolution of flying ad hoc networks (FANETs). However, the high movement of unmanned aerial vehicles (UAVs) in these networks causes iterated failures of communication links and constant changes in