The rapid growth of peer-to-peer transportation solutions for real-time food delivery and ride-hailing services has transformed urban mobility and local commerce. Despite significant research on platform optimization, limited attention has been given to the behavioral dynamics of delivery drivers. This study employs a dynamic discrete choice modeling approach using a Markov decision process to investigate the decision-making process of delivery drivers when accepting or rejecting order requests. Using a dataset from Meituan, a major delivery platform based in China, we examine how spatial, temporal, and operational factors influence drivers' order acceptance decisions. We demonstrate the importance of considering both the dynamic nature of the decision process and of incorporating individual-specific heterogeneity to account for correlations between the decisions made by the same individual over time. The research reveals critical insights into driver behavior, demonstrating that order acceptance is a sophisticated, forward-looking process. Key findings include a significant preference for pre-booked orders, varying trip length preferences across peak and off-peak periods, and the importance of maintaining an optimal number of concurrent orders. The research contributes to the understanding of driver decision-making in urban transportation systems and provides actionable recommendations for improving platform efficiency and driver satisfaction.

This paper presents a framework for estimating the capacity of a multimodal maritime port system handling vessels of multiple classes. Port system capacity can be categorized into two distinct types: operating capacity, defined as the maximum number of vessels that can be processed over an extended period under stable operating conditions, and ultimate capacity, defined as the absolute maximum vessel throughput achievable irrespective of stability. Distinguishing between these two capacity measures is critical for long-term planning and resilience analysis, as ports may temporarily operate above sustainable levels following disruptions or during demand surges. Despite the importance of this distinction, existing port capacity models generally do not provide methods to compute port-level capacity estimates that clearly differentiate between operating and ultimate capacity. We introduce methods to estimate both capacity measures for seaport systems. We apply the proposed framework using the Port of Houston, Texas as a case study. Operating capacity is estimated using a parsimonious queueing-theoretic model, while ultimate capacity is estimated by fitting an ordinary differential equation model to simulation outputs. We estimate an operating capacity of approximately 0.9 vph and an ultimate capacity of approximately 1.4 vph for the Port of Houston. Sensitivity analysis of key port resources indicates that liquid-bulk terminals constitute the primary bottlenecks under stable operating conditions, whereas pilot availability becomes the dominant bottleneck following disruptions. These methods can be used in port planning to determine the expected operational and resilience gains of a given infrastructure intervention, or to identify bottlenecks in a complex, multimodal port environment.

Interdependencies among infrastructure systems may amplify the impact of disruptive events. This paper presents a network-based repair sequencing and resilience assessment model to examine the influence of two-way interdependency between road transportation networks and power distribution networks. We use a modified IEEE 33-bus power network and the Sioux Falls road network (both standard testbed instances) to simulate the effects of a natural disaster, with a single repair crew repairing damaged nodes. Power failures cause delays in the transportation system due to signal outages, and delays in the transportation system affect the times when the repair crew can reach damaged nodes. We use a simulated annealing algorithm heuristic to find good repair sequences that account for both types of interactions between power and road systems. We compare the solutions obtained from the heuristic algorithm to alternative strategies and results indicate that the interdependency-aware strategy achieves faster restoration times and enhanced overall resilience, compared to random, priority-based, and interdependency-naive strategies.

Vickrey's classic single-bottleneck departure time choice equilibrium model exhibits instability under many plausible day-to-day learning dynamics. Such instability is not observed in reality, so does this difference stem from the day-to-day dynamics or from one of the simplifying assumptions of the basic model? This paper explores a variant of the basic model with a continuous distribution of schedule delay parameters, which we intuitively expect to have more favorable stability properties. To attain tractability, we assume a monotonic relationship between earliness and lateness parameters. We first verify the existence and uniqueness of the equilibrium solution for this model. We then study a broad class of day-to-day dynamics satisfying local pressure and order preservation conditions. Our main contribution is a formal proof that, surprisingly, all such day-to-day dynamics in this context are unstable.

Shared autonomous electric vehicles can provide on-demand transportation for passengers while also interacting extensively with the electric distribution system. This interaction is especially beneficial after a disaster when the large battery capacity of the fleet can be used to restore critical electric loads. We develop a dispatch policy that balances the need to continue serving passengers (especially critical workers) and the ability to transfer energy across the network. The model predictive control policy tracks both passenger and energy flows and provides maximum passenger throughput if any policy can. The resulting mixed integer linear programming problem is difficult to solve for large-scale problems, so a distributed solution approach is developed to improve scalability, privacy, and resilience. We demonstrate that the proposed heuristic, based on the alternating direction method of multipliers, is effective in achieving near-optimal solutions quickly. The dispatch policy is examined in simulation to demonstrate the ability of vehicles to balance these competing objectives with benefits to both systems. Finally, we compare several dispatch behaviors, demonstrating the importance of including operational constraints and objectives from both the transportation and electric systems in the model.

This book covers static and dynamic traffic assignment models used in transportation planning and network analysis. Traffic assignment is the final step in the traditional planning process, and recent decades have seen many advances in formulating and solving such models. The book discusses classical solution methods alongside recent ones used in contemporary planning software. The primary audience for the book is graduate students new to transportation network analysis, and to this end there are appendices providing general mathematical background, and more specific background in formulating optimization problems. We have also included appendices discussing more general optimization applications outside of traffic assignment. We believe the book is also of interest to practitioners seeking to understand recent advances in network analysis, and to researchers wanting a unified reference for traffic assignment content. A second volume is currently under preparation, and will cover transit, freight, and logistics models in transportation networks. A free PDF version of the text will always be available online at https://sboyles.github.io/blubook.html. We will periodically post updated versions of the text at this link, along with slides and other instructor resources.

Transportation network recovery after an extreme hazard or natural disaster is time sensitive and resource intensive, with hundreds or thousands of damaged links needing repair. The associated optimization problem is difficult, and experiments in the published literature are largely confined to smaller-scale instances. We aim to bridge this gap by comparing eight algorithms proposed for repair sequencing on larger-scale instances. These methods include algorithms proposed in prior literature, an improved bidirectional beam search heuristic, and a simulated annealing heuristic newly tailored for network repair sequencing. Our experiments involved over 1,900 random problem instances over two test networks with 8-64 broken links, significantly greater than what has been reported in past literature. We assessed the solution quality and computational needs of these methods. In particular, our simulated annealing heuristic offers high-quality solutions in less than a day for problems with up to 175 and 185 broken links on the Anaheim and Berlin-Mitte-Center test networks, respectively, corresponding to 18%-20% of network links. We also show transferability of the simulated annealing heuristic by tuning its parameters on the Anaheim network, then applying it without further tuning to Berlin-Mitte-Center. Comparable performance was obtained on both networks.

Automatic vehicle location (AVL) data offers insights into transit dynamics, but its effectiveness is often hampered by inconsistent update frequencies, necessitating trajectory reconstruction. This research evaluates 13 trajectory reconstruction methods, including several novel approaches, using high-resolution AVL data from Austin, Texas. We examine the interplay of four critical factors – velocity, position, smoothing, and data density – on reconstruction performance. A key contribution of this study is evaluation of these methods across sparse and dense datasets, providing insights into the trade-off between accuracy and resource allocation. Our evaluation framework combines traditional mathematical error metrics for positional and velocity with practical considerations, such as physical realism (e.g., aligning velocity and acceleration with stopped states, deceleration rates, and speed variability). In addition, we provide insight into the relative value of each method in calculating realistic metrics for infrastructure evaluations. Our findings indicate that velocity-aware methods consistently outperform position-only approaches. Interestingly, we discovered that smoothing-based methods can degrade overall performance in complex, congested urban environments, although enforcing monotonicity remains critical. The velocity constrained Hermite interpolation with monotonicity enforcement (VCHIP-ME) yields optimal results, offering a balance between high accuracy and computational efficiency. Its minimal overhead makes it suitable for both historical analysis and real-time applications, providing significant predictive power when combined with dense datasets. These findings offer practical guidance for researchers and practitioners implementing trajectory reconstruction systems and emphasize the importance of investing in higher-frequency AVL data collection for improved analysis.

Increasing opportunities for telework and other forms of online activity participation are changing the landscape of transportation demand. This paper presents a relaxed singly constrained static traffic assignment model that extends existing approaches to accommodate both destination choice and the decision not to travel. We demonstrate that this formulation maintains desirable properties of existence and uniqueness of solutions, while being more flexible in capturing travel behaviors. A case study on the Austin, Texas, network examines scenarios related to telework adoption, targeted development in low-income areas, and changes to central business district attractiveness. Results show the model captures important behavioral shifts not reflected in simpler approaches such as models allowing destination choice or elastic demand alone. Using the relaxed singly constrained model, we demonstrate non-uniform traffic impacts across the network and find tradeoffs between congestion reduction and economic activity. The case study highlights potential equity impacts of both overall increases in telework and targeted changes in destination attractiveness that would otherwise be overlooked, suggesting that it is critical to develop more sophisticated models integrating traffic assignment with demand-side predictions.

Large-scale evacuations from natural disasters such as hurricanes pose major logistical and operational challenges. There is often insufficient roadway capacity for entire populations to evacuate immediately, causing costly and potentially deadly delays. Traffic monitoring devices (TMDs) can help ensure an evacuation proceeds smoothly. Information collected by such devices can help authorities direct traffic onto underutilized routes and dispatch emergency services to clear traffic incidents faster. Closely monitoring every roadway in the system is prohibitively expensive, so we propose an efficient quantitative method to identify links that would most benefit the system by being monitored; we define these critical locations as roadway segments where any delay or underutilization of capacity will increase the overall evacuation time. We test this method in two case study hurricane scenarios along the Texas coast, and demonstrate how monitoring these critical locations could reduce delays if the information collected leads to faster incident clearance times and information provided to drivers improves their routing decisions. The simulation indicates that routing decisions have a larger impact on performance than incident detection and that effective traffic monitoring and route guidance can reduce clearance time for a large-scale evacuation in the Houston region by 19.1%.

Understanding how evacuees use real-time traffic information is crucial for developing effective emergency evacuation response plans for hurricane-prone areas. This paper investigates how such data were used during past hurricane evacuations and post-evacuation returns in Texas with a survey dataset collected between August 2022 and February 2023. We examined the usage patterns of various platforms, including navigation apps, social media, TV, radio, and information provided by public agencies. We found that a larger household size, longer distance to evacuation destinations, and past experience with hurricane evacuations are associated with greater use of real-time information platforms. Experienced evacuees tend to rely on navigation apps and social media, and those with experience before 2010 are more inclined to use the TV and radio as their primary sources of information. Motivation for using these platforms varies among users of different platforms. Although both navigation app users and social media users value their familiarity with the platform, the former also prioritize the convenience of using it. It was also found that TV users prioritize service accessibility, radio users emphasize service availability, and users of official agency information sources place a high value on data accuracy. These findings have implications for policymakers, emergency planners, and traffic engineers involved with disaster response operations to improve the resilience of transportation systems.

Shared autonomous electric vehicles (SAEVs) can provide on demand transportation for passengers while also interacting extensively with the electric distribution system. This interaction is especially beneficial after a disaster when the large battery capacity of the fleet can be used to restore critical electric loads. This study develops a dispatch policy that balances the need to continue serving passengers (especially critical workers) and the ability to transfer energy across the network. Power flows are modeled using the LinDistFlow model, while transportation network is incorporated through a queuing model. The resulting model predictive control algorithm is examined in simulation to demonstrate the ability of vehicles to balance these competing objectives with benefits to both systems.

Drones and electric vehicles (EVs) represent promising technologies for enhancing the efficiency and sustainability of last-mile delivery services. This paper focuses on the optimization of customer deliveries through the integration of a plug-in hybrid electric vehicle (PHEV) and a drone. Our model, named the plug-in hybrid electric vehicle traveling-salesman problem with drone (PHEVTSPD), assumes the PHEV can be recharged, either fully or partially, at charging stations, while the drone can be launched or retrieved from the EV. Both the EV and drone are capable of independently serving the customer. In comparison with traditional truck-only or drone-only delivery models, the hybrid EV-drone model overcomes the limitations of drone payload capacity and EV service area, thereby significantly improving delivery efficiency and reducing greenhouse-gas emissions. This research presents a three-index mixed-integer linear program (MILP) formulation of PHEVTSPD. Additionally, a linear or piecewise linear approximation of the concave time-state-of-charge (SoC) function is adopted in the model. To solve the proposed problem, we introduce an adaptive large-neighborhood search (ALNS) metaheuristic. Numerical analysis results reveal that the proposed ALNS method outperforms variable neighborhood search (VNS) with an average optimality gap of approximately 3% when solving instances with 10 nodes. Furthermore, a piecewise linear function with a six-line-segment approximation demonstrates an average of 10.8% lower cost compared with a linear approximation.

This article discusses a robust network interdiction problem considering uncertainties in arc capacities and resource consumption. The problem involves two players: an adversary seeking to maximize the flow of a commodity through the network and an interdictor whose objective is to minimize this flow. The interdictor plays first and selects network arcs to interdict, subject to a resource constraint. The problem is formulated as a bilevel problem, and an upper bound single level mix-integer linear formulation is derived. The upper bound formulation is solved using three heuristics tailored for this problem and the network structure, based on Lagrangian relaxation and Benders’ decomposition. On average, each heuristic provides a reduction in run time of at least 85% compared to a state-of-the-art solver. Enhanced Benders’ decomposition achieves a solution with an optimality gap of less than 5% for all tested instances. Sensitivity analyses are conducted for the level of uncertainty in network parameters and the uncertainty budget. Robust decisions are also compared to decisions not accounting for uncertainty to evaluate the value of robustness, showing a reduction in simulation maximum flows by as much as 89.5%.

Numerous government and non-governmental agencies are increasing their efforts to better quantify the disproportionate effects of climate risk on vulnerable populations with the goal of creating more resilient communities. Sociodemographic based indices have been the primary source of vulnerability information the past few decades. However, using these indices fails to capture other facets of vulnerability, such as the ability to access critical resources (e.g., grocery stores, hospitals, pharmacies, etc.). Furthermore, methods to estimate resource accessibility as storms occur (i.e., in near-real time) are not readily available to local stakeholders. We address this gap by creating a model built on strictly open-source data to solve the user equilibrium traffic assignment problem to calculate how an individual's access to critical resources changes during and immediately after a flood event. Redundancy, reliability, and recoverability metrics at the household and network scales reveal the inequitable distribution of the flood's impact. In our case-study for Austin, Texas we found that the most vulnerable households are the least resilient to the impacts of floods and experience the most volatile shifts in metric values. Concurrently, the least vulnerable quarter of the population often carries the smallest burdens. We show that small and moderate inequalities become large inequities when accounting for more vulnerable communities' lower ability to cope with the loss of accessibility, with the most vulnerable quarter of the population carrying four times as much of the burden as the least vulnerable quarter. The near-real time and open-source model we developed can benefit emergency planning stakeholders by helping identify households that require specific resources during and immediately after hazard events.

This study proposes a multi-period facility location formulation to maximize coverage while meeting a coverage reliability constraint. The coverage reliability constraint is a chance constraint limiting the probability of failure to maintain the desired service standard, commonly followed by emergency medical services and fire departments. Further, uncertainties in the failure probabilities are incorporated by utilizing robust optimization using polyhedral uncertainty sets, which results in a compact mixed-integer linear program. A case study in the Portland, OR metropolitan area is analyzed for employing unmanned aerial vehicles (UAVs) or drones to deliver defibrillators in the region to combat out-of-hospital cardiac arrests. In the context of this study, multiple periods represent periods with different wind speed and direction distributions. The results show that extending to a multi-period formulation, rather than using average information in a single period, is particularly beneficial when either response time is short or uncertainty in failure probabilities is not accounted for. Accounting for uncertainty in decision-making improves coverage significantly while also reducing variability in simulated coverage, especially when response times are longer. Going from a single-period deterministic formulation to a multi-period robust formulation boosts the simulated coverage values by 57%, on average. The effect of considering a distance-based equity metric in decision-making is also explored.

This article introduces a departure time choice model where delays are separable. This contrasts with the standard Vickrey single-bottleneck model, with a queue that persists over the peak period. The resulting model has more of the flavor of the static traffic assignment problem, but with a temporal focus, rather than spatial. Our primary motivation is to clearly determine whether the higher price of anarchy in the single-bottleneck model (compared to static traffic assignment) is due to the distinction between separable and nonseparable delay models, or due to the distinction between temporal and spatial modeling. In this regard, we show that separability is the critical factor; indeed, the price of anarchy in our temporal choice model with separable delay is even lower than in the static traffic assignment problem. A secondary motivation is to represent temporal choice behavior in undersaturated traffic systems that nevertheless experience flow-dependent delays. To this end, we develop a “hybrid” model capable of modeling a traffic system as it transitions from undersaturated behavior (separable) to oversaturated behavior with a persistent queue (nonseparable), and analyze the resulting equilibria.

We study the mean‐standard deviation minimum cost flow (MSDMCF) problem, where the objective is minimizing a linear combination of the mean and standard deviation of flow costs. Due to the nonlinearity and nonseparability of the objective, the problem is not amenable to the standard algorithms developed for network flow problems. We prove that the solution for the MSDMCF problem coincides with the solution for a particular mean‐variance minimum cost flow (MVMCF) problem. Leveraging this result, we propose bisection (BSC), Newton–Raphson (NR), and a hybrid (NR‐BSC)—method seeking to find the specific MVMCF problem whose optimal solution coincides with the optimal solution for the given MSDMCF problem. We further show that this approach can be extended to solve more generalized nonseparable parametric minimum cost flow problems under certain conditions. Computational experiments show that the NR algorithm is about twice as fast as the CPLEX solver on benchmark networks generated with NETGEN.