With the growth of cities and the expansion of urban populations, traffic congestion has become a major challenge in urban areas. Congestion significantly worsens economic and environmental conditions, and is particularly problematic in areas with heavy truck traffic. In this paper, we introduce a coordinated freight routing system aimed at optimizing the overall utility of the network and alleviating congestion through personalized routing instructions and incentives. This system specifically tailors the allocation of incentives and payments to individual drivers, considering both current traffic conditions and their specific routing pReferences. We employ a mixed logit model with a linear utility specification to model drivers’ route choice preferences and decisions. Participation in the system is voluntary, and the system ensures that for most drivers, the combined expected utility, including incentives, surpasses their anticipated utility under User Equilibrium (UE), thereby motivating a substantial number of drivers to follow the assigned routes. The system collects data on the drivers’ routing choices, subsequently updating estimates of utility parameters based on their recent decisions. Ahierarchical Bayes estimator is used for the estimation of individual-specific utility parameters. By integrating driver behavior into the routing process, our system actively adapts and updates parameters in response to real driver actions, offering a dynamic and accurate representation of evolving driver preferences. Numerical experiments on the Sioux Falls network demonstrate the effectiveness of the proposed method.

Traffic congestion has become an inevitable challenge in large cities due to population increases and the expansion of urban areas. Various approaches are introduced to mitigate traffic issues, encompassing from expanding the road infrastructure to employing demand management. Congestion pricing and incentive schemes are extensively studied for traffic control in traditional networks where each driver/rider is a network “player”. In this setup, drivers’/riders’ “selfish” behavior hinders the network from reaching a socially optimal state. In future mobility services, on the other hand, a large portion of drivers/vehicles may be controlled by a small number of companies/organizations. In such a system, offering incentives to organizations can potentially be much more effective in reducing traffic congestion rather than offering incentives directly to drivers. This paper studies the problem of offering incentives to organizations to change the behavior of their individual drivers (or individuals relying on the organization’s services). We developed a model where incentives are offered to each organization based on their aggregated travel time loss across all drivers/riders in that organization. Such an incentive offering mechanism requires solving a large-scale optimization problem to minimize the system-level travel time. We propose an efficient algorithm for solving this optimization problem. Numerous experiments on Los Angeles County traffic data reveal the ability of our method to reduce system-level travel time by up to 7.15%. Moreover, our experiments show that incentivizing organizations can be up to 7 times more cost-effective than incentivizing individual drivers when aiming for maximum travel time reduction.

Traffic congestion has a negative economic and environmental impact. Traffic conditions become even worse in areas with high volume of trucks. In this paper, we propose a coordinated pricing-and-routing scheme for truck drivers to efficiently route trucks into the network and improve the overall traffic conditions. A basic characteristic of our approach is the fact that we provide personalized routing instructions based on drivers’ individual routing preferences. In contrast with previous works that provide personalized routing suggestions, our approach optimizes over a total system-wide cost through a combined pricing-and-routing scheme that satisfies the budget balance on average property and ensures that every truck driver has an incentive to participate in the proposed mechanism by guaranteeing that the expected total utility of a truck driver (including payments) in case he/she decides to participate in the mechanism, is greater than or equal to his/her expected utility in case he/she does not participate. Since estimating a utility function for each individual truck driver is computationally intensive, we first divide the truck drivers into disjoint clusters based on their responses to a small number of binary route choice questions and we subsequently propose to use a learning scheme based on the Maximum Likelihood Estimation (MLE) principle that allows us to learn the parameters of the utility function that describes each cluster. The estimated utilities are then used to calculate a pricing-and-routing scheme with the aforementioned characteristics. Simulation results in the Sioux Falls network demonstrate the efficiency of the proposed pricing-and-routing scheme.

The current freight transportation network is highly unbalanced as routing decisions are made by individual users without coordination. Certain routes may become congested when chosen based on current traffic information without any anticipation that if other users do the same, these routes may become congested. In this paper we show how a centrally coordinated load balancing system that considers all vehicles to be diesel can take into account electric trucks as mixed fleets. The electric trucks impose additional constraints due to the limitation of range, charging time of batteries as well as the dependency of the battery charge on traffic conditions. The use of a co-simulation approach as part of the system accounts for these nonlinear dependencies and provides more realistic cost estimates for the optimization part. Traffic simulation results using a realistic road network reveal the benefits of applying load balancing and show that as the number of electric trucks increases, the emissions reduce; however, due to the cost of charging, their operational costs are not necessarily less than those of the corresponding diesel trucks. For the electric trucks to compete with diesel, charging should occur when drivers are off duty since the cost of charging includes the labor cost of the waiting driver. It is also shown that a centrally coordinated truck routing system that considers the characteristics of electric trucks in mixed fleets can reduce the operational cost of trucks and encourage the deployment of electric trucks in order to reduce emissions and improve air quality.

The efficient use of the road network for freight transport has a big impact on travel times, pollution, and fuel consumption, as well as on the mobility of passenger vehicles. In today's road network, truck drivers make uncoordinated selfish routing decisions, which may easily congest an initially uncongested route as many truck drivers make the same selfish decision by choosing the same route in an effort to minimize their travel time without accounting for the fact that others do the same, given the same available traffic information. In this paper, we propose a coordinated system for truck drivers, using monetary incentives and fees, to balance the traffic load and improve the overall traffic conditions and time delays experienced by both truck and passenger vehicle drivers. The basic characteristics of the mechanisms presented are that they are budget balanced, do not penalize the truck drivers compared to the user equilibrium, and they assume voluntary participation. Two models of voluntary participation are considered: weak and strong voluntary participation. In the first, each one of the drivers prefers all the drivers (including self) to participate in the mechanism than not. In the second model, each one of the truck drivers prefers to participate in the system, provided that all the others do. For each model of voluntary participation, an incentive mechanism is designed. A special emphasis is given to the fairness of the proposed mechanisms. The numerical examples are used to demonstrate the results and the efficiency of the solution techniques.

The sharp increase in e-commerce over the last few years has led to an increase in the volume of trucks both in ports and in commercial areas. Truck traffic has a negative impact on traffic flow in general due to the size of trucks and their slower dynamics. The continuously increasing use of navigation apps has led drivers to make their routing decisions in an independent manner in an effort to minimize their own individual travel time, with possible significant deviation from a socially optimum solution. In this paper, we consider the use of coordinated routing in order to achieve load balancing. Users send their OD matrices as well as their preferred departure time to the coordinator who gives them routing instructions based on a socially optimum cost. This design enables us to derive sufficient conditions under which we prove the existence of mechanisms which are truthful in equilibrium, budget balanced on average and create individual incentives for voluntary participation of the truck drivers. Subsequently, we design our mechanism in a way that only uses a minimal set of sufficient conditions in order to guarantee the existence of a solution and maximize its efficiency. Finally, the extensive simulation results of our approach in the Braess and the Sioux Falls networks demonstrate that the proposed mechanism can approach the system optimum solution.

The complexity and dynamics of multimodal freight transportation networks make the optimum routing of freight demand a challenging task. Route decision-making in a dynamical and complex urban multimodal transportation environment aims to minimize a certain objective cost relying on the accurate prediction of the traffic network states and the estimation of the route costs that are not readily available. The purpose of this paper is to develop a methodology to be used by a central coordinator who generates individual routing decisions for shippers by minimizing overall cost, assuming that all participating shippers send their demands to this central coordinator. We propose, analyze, and evaluate a multimodal freight routing system with hard vehicle availability and capacity constraints based on a hierarchical Co-Simulation Optimization (COSMO) approach. The COSMO approach consists of a simulation layer that provides traffic state predictions and cost estimations to an upper optimization layer that incorporates a load balancing methodology to speed up the convergence of the optimization algorithm. A simulation test bed consisting of a road traffic simulation and a rail simulation model for the Los Angeles/Long Beach Ports regional area is developed and is used to demonstrate the efficiency of the proposed approach.

The urban traffic network has temporal and spatial characteristics whose changing conditions have often unpredictable effects on the flow of loads that include passengers and freight. As a result, the current traffic network is unbalanced, leading to high and low peaks of traffic in both time and space. The freight transportation chain can utilize these high and low peaks in the road and rail network in order to utilize more effectively available capacity. The purpose of this paper is to develop a coordinated multimodal dynamic freight load balancing (MDFLB) system to balance freight loads across the rail and road network. The MDFLB system collects and updates information from all the shipping companies and assigns freight loads to the available carriers using an optimization model while taking into account current and predicted dynamical changes in the associated networks. Since the freight loads can change the assumed states of the network, namely, the link travel times, which could then render the solution of the optimization problem no longer optimum, an iterative approach is considered involving online network simulation models. The simulation models are used to test and modify the optimization-based load balancing solution and estimate the new states of the network used by the optimizer. This feedback iterative approach guarantees that the overall cost function is non-increasing and it stops when it converges to a minimum or when a stopping criterion is satisfied depending on the time horizon of interest. A simulation case study that focuses on distribution of freight in an area that includes the two major sea ports in Southern California is used to demonstrate the effectiveness of the proposed coordinated MDFLB.

Demand-responsive transport (DRT) systems provide flexible transport services for passengers who request door-to-door rides in shared-ride mode without fixed routes and schedules. DRT systems face interesting coordination challenges. For example, one has to design cost-sharing mechanisms for offering fare quotes to potential passengers so that all passengers are treated fairly. The main issue is how the operating costs of the DRT system should be shared among the passengers (given that different passengers cause different amounts of inconvenience to the other passengers), taking into account that DRT systems should provide fare quotes instantaneously without knowing future ride request submissions. We determine properties of cost-sharing mechanisms that make DRT systems attractive to both the transport providers and passengers, namely online fairness, immediate response, individual rationality, budget balance, and ex-post incentive compatibility. We propose a novel cost-sharing mechanism, which is called Proportional Online Cost Sharing (POCS), which provides passengers with upper bounds on their fares immediately after their ride request submissions despite missing knowledge of future ride request submissions, allowing them to accept their fare quotes or drop out. We examine how POCS satisfies these properties in theory and computational experiments.

Abstract In this article, we are interested in routing vehicles to service a large‐scale bioterrorism emergency. We describe the specifics of routing vehicles in such a large‐scale emergency and decompose the problem into two stages: a planning stage and an operational stage. In the planning stage, we generate the routes well in advance of any emergency. In the operational stage, we take into account the planned routes and the information revealed at the time of the emergency, to decide the delivery quantity and any adjustments to the routes. We propose mathematical formulations and solution approaches for both stages. Lastly, we demonstrate the effectiveness of our formulations and solution procedures in developing robust routes through numerical experiments. © 2009 Wiley Periodicals, Inc. NETWORKS, 2009

Route planning in uncertain and dynamic networks has recently emerged as an active and intense area of research, both due to industry needs and technological advances. This paper investigates methods to predict travel times along the arcs and estimate arrival times at the nodes of a stochastic and dynamic network in real time. It is shown that, under fairly mild conditions, the developed travel and arrival time estimators are unbiased and that the error variance of the arrival time estimator is bounded. Simulation results are used to demonstrate the efficiency of the proposed algorithm.

Most existing methods for truck route planning assume known static data in an environment that is time varying and uncertain by nature, which limits their widespread applicability. The development of intelligent transportation systems such as the use of information technologies reduces the level of uncertainties and makes the use of more appropriate dynamic formulations and solutions feasible. In this paper, a truck route planning problem called stochastic traveling salesman problem with time windows (STSPTW) in which traveling times along roads and service times at customer locations are stochastic processes is investigated. A methodology is developed to estimate the truck arrival time at each customer location. Using estimated arrival times, an approximate solution method based on dynamic programming is proposed. The algorithm finds the best route with minimum expected cost while it guarantees certain levels of service are met. Simulation results are used to demonstrate the efficiency of the proposed algorithm

A distributed control approach based on multiagent negotiation is presented, wherein stops and buses act as agents that communicate in real-time to achieve dynamic coordination of bus dispatching at various stops. The negotiation between a Bus Agent and a Stop Agent is conducted based on marginal cost calculations. We present optimality conditions for the formulated problem, using a negotiation algorithm, which we derive, to coordinate bus holding at various stops. A comparison between the negotiation algorithm and other simple bus control strategies such as on-schedule and even-headway strategies made through simulations verifies the robustness and efficiency of our negotiation strategy to different transit environments, involving both stationary passenger arrivals as well as a variety of nonstationary passenger arrivals.

The scheduling of vehicles for demand responsive transportation systems is a classic combinatorial optimization problem. With the passage of the Americans with Disabilities Act, which requires that transit agencies provide para-transit or on demand service for the disabled, there has been renewed interest in demand responsive transit. In this paper, we review the state-of-the-art in the literature on scheduling demand responsive systems and present a new heuristic for real-time scheduling of such systems. We evaluate the heuristics using data provided by para-transit service providers in Los Angeles County.