Highway Capacity Manual改訂の動向--テイラ-教授の講演より

Nonlinear Programming: A Unified Approach

Annual Energy Outlook

In the field of transportation, data analysis is probably the most important and widely used research tool available. In the data analysis universe, there are two ‘schools of thought’; the first uses statistics as the tool of choice, while the second – one of the many methods from – Computational Intelligence. Although the goal of both approaches is the same, the two have kept each other at arm’s length. Researchers frequently fail to communicate and even understand each other’s work. In this paper, we discuss differences and similarities between these two approaches, we review relevant literature and attempt to provide a set of insights for selecting the appropriate approach.

Statistical methods versus neural networks in transportation research: Differences, similarities and some insights

Driven by new laws and regulations concerning the emission of greenhouse gases, carriers are starting to use electric vehicles for last-mile deliveries. The limited battery capacities of these vehicles necessitate visits to recharging stations during delivery tours of industry-typical length, which have to be considered in the route planning to avoid inefficient vehicle routes with long detours. We introduce the electric vehicle-routing problem with time windows and recharging stations E-VRPTW, which incorporates the possibility of recharging at any of the available stations using an appropriate recharging scheme. Furthermore, we consider limited vehicle freight capacities as well as customer time windows, which are the most important constraints in real-world logistics applications. As a solution method, we present a hybrid heuristic that combines a variable neighborhood search algorithm with a tabu search heuristic. Tests performed on newly designed instances for the E-VRPTW as well as on benchmark instances of related problems demonstrate the high performance of the heuristic proposed as well as the positive effect of the hybridization.

The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations

This paper develops an equilibrium modeling framework that captures the interactions among availability of public charging opportunities, prices of electricity, and destination and route choices of plug-in hybrid electric vehicles (PHEVs) at regional transportation and power transmission networks coupled by PHEVs. The modeling framework is then applied to determine an optimal allocation of a given number of public charging stations among metropolitan areas in the region to maximize social welfare associated with the coupled networks. The allocation model is formulated as a mathematical program with complementarity constraints, and is solved by an active-set algorithm. Numerical examples are presented to demonstrate the models and offer insights on the equilibrium of the coupled transportation and power networks, and optimally allocating resource for public charging infrastructure.

Optimal deployment of public charging stations for plug-in hybrid electric vehicles

Many decision-making problems in transportation system planning and management can be formulated as bilevel programming models, which are intrinsically nonconvex and hence difficult to solve for the global optimum. Therefore, successful implementations of bilevel models rely largely on the development of an efficient algorithm in handling realistic complications. In spite of various intriguing attempts that were made in solving the bilevel models, these algorithms are unfortunately either incapable of finding the global optimum or very computationally intensive and impractical for problems of a realistic size. In this paper, a genetic-algorithms-based (GAB) approach is proposed to efficiently solve these models. The performance of the algorithm is illustrated and compared with the previous sensitivity-analysis-based algorithm using numerical examples. The computation results show that the GAB approach is efficient and much simpler than previous heuristic algorithms. Furthermore, it is believed that the GAB approach can more likely achieve the global optimum based on the globality and parallelism of genetic algorithms.

Genetic-Algorithms-Based Approach for Bilevel Programming Models

Ride-sourcing refers to an emerging urban mobility service that private car owners drive their own vehicles to provide for-hire rides. This paper analyzes the ride-sourcing market using an aggregate model where the matchings between customers and drivers are captured by an exogenous matching function. It is found that without any regulatory intervention a monopoly ride-sourcing platform will maximize the joint profit with its drivers. On the other hand, the first-best solution is not sustainable when the matching function exhibits increasing returns to scale and the cost function of the platform is subject to economies of scale. Regardless of the examined market scenarios, the average waiting time of customers is proportional to the average searching time of drivers. We establish conditions for regulators to solely regulate the commission charged by the platform to guarantee the second-best. We further investigate the competition of ride-sourcing platforms and find that competition does not necessarily lower the price level or improve social welfare. In the latter case, regulators may rather encourage the merger of the platforms and regulate them directly as a monopolist.

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Economic Analysis of Ride-sourcing Markets

This paper explores how to optimally locate public charging stations for electric vehicles on a road network, considering drivers’ spontaneous adjustments and interactions of travel and recharging decisions. The proposed approach captures the interdependency of different trips conducted by the same driver by examining the complete tour of the driver. Given the limited driving range and recharging needs of battery electric vehicles, drivers of electric vehicles are assumed to simultaneously determine tour paths and recharging plans to minimize their travel and recharging time while guaranteeing not running out of charge before completing their tours. Moreover, different initial states of charge of batteries and risk-taking attitudes of drivers toward the uncertainty of energy consumption are considered. The resulting multi-class network equilibrium flow pattern is described by a mathematical program, which is solved by an iterative procedure. Based on the proposed equilibrium framework, the charging station location problem is then formulated as a bi-level mathematical program and solved by a genetic-algorithm-based procedure. Numerical examples are presented to demonstrate the models and provide insights on public charging infrastructure deployment and behaviors of electric vehicles.

Deploying public charging stations for electric vehicles on urban road networks

This paper addresses issues relevant to internalizing traffic emission externality on road networks with fixed travel demands. In addition to marginal social cost pricing (MSC) congestion pricing schemes are considered that yield a traffic flow distribution with minimum total travel time as first-best. We give a counterexample showing that MSC and other first-best congestion pricing schemes do not necessarily lead to less traffic emissions. Without using any subsidy, we indicate that there always exists a charging scheme that induces a traffic flow distribution with minimum emissions. We also provide a bound on the percent reduction in traffic emissions achievable by any charging scheme, and then offer methods for computing charges in a manner that allows decision makers to trade-off between two conflicting objectives, alleviating congestion versus reducing traffic emissions.

Yafeng Yin

Papers

Optimal deployment of public charging stations for plug-in hybrid electric vehicles

Genetic-Algorithms-Based Approach for Bilevel Programming Models

Economic Analysis of Ride-sourcing Markets

Deploying public charging stations for electric vehicles on urban road networks

Internalizing Emission Externality on Road Networks