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

Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

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Urban Computing: Concepts, Methodologies, and Applications

A review of definitions and measures of system resilience

On the truck dispatching problem

The orienteering problem: A survey

A Green Vehicle Routing Problem (G-VRP) is formulated and solution techniques are developed to aid organizations with alternative fuel-powered vehicle fleets in overcoming difficulties that exist as a result of limited vehicle driving range in conjunction with limited refueling infrastructure The G-VRP is formulated as a mixed integer linear program Two construction heuristics, the Modified Clarke and Wright Savings heuristic and the Density-Based Clustering Algorithm, and a customized improvement technique, are developed Results of numerical experiments show that the heuristics perform well Moreover, problem feasibility depends on customer and station location configurations Implications of technology adoption on operations are discussed

A Green Vehicle Routing Problem

http://www.citsm.umd.edu/documents/final-reports/MillerHooks%20Zhang%20Faturechi%202011%20Final%20Report.pdf

Measuring and maximizing resilience of freight transportation networks

Astochastic, mixed-integer program (MIP) involving joint chance constraints is developed that generates least-cost vehicle redistribution plans for shared-vehicle systems such that a proportion of all near-term demand scenarios are met. The model aims to correct short-term demand asymmetry in shared-vehicle systems, where flow from one station to another is seldom equal to the flow in the opposing direction. The model accounts for demand stochasticity and generates partial redistribution plans in circumstances when demand outstrips supply. This stochastic MIP has a nonconvex feasible region. A novel divide-and-conquer algorithm for generating p-efficient points, used to transform the problem into a set of disjunctive, convex MIPs and handle dual-bounded chance constraints, is proposed. Assuming independence of random demand across stations, a faster cone-generation method is also presented. In a real-world application for a system in Singapore, the potential of redistribution as a fleet management strategy and the value of accounting for inherent stochasticities are demonstrated.

Fleet Management for Vehicle Sharing Operations

In this paper, an indicator of network resilience is defined that quantifies the ability of an intermodal freight transport network to recover from disruptions due to natural or human-caused disaster. The indicator considers the network's inherent ability to cope with the negative consequences of disruptions as a result of its topological and operational attributes. Furthermore, the indicator explicitly accounts for the impact of potential recovery activities that might be taken in the immediate aftermath of the disruption to meet target operational service levels while adhering to a fixed budget. A stochastic mixed-integer program is proposed for quantifying network resilience and identifying an optimal postevent course of action (i.e., set of activities) to take. To solve this mathematical program, a technique that accounts for dependencies in random link attributes based on concepts of Benders decomposition, column generation, and Monte Carlo simulation is proposed. Experiments were conducted to illustrate the resilience concept and procedure for its measurement, and to assess the role of network topology in its magnitude.

Resilience: An Indicator of Recovery Capability in Intermodal Freight Transport

We consider stochastic, time-varying transportation networks, where the arc weights (arc travel times) are random variables with probability distribution functions that vary with time. Efficient procedures are widely available for determining least time paths in deterministic networks. In stochastic but time-invariant networks, least expected time paths can be determined by setting each random arc weight to its expected value and solving an equivalent deterministic problem. This paper addresses the problem of determining least expected time paths in stochastic, time-varying networks. Two procedures are presented. The first procedure determines the a priori least expected time paths from all origins to a single destination for each departure time in the peak period. The second procedure determines lower bounds on the expected times of these a priori least expected time paths. This procedure determines an exact solution for the problem where the driver is permitted to react to revealed travel times on traveled links en route, i.e., in a time-adaptive route choice framework. Modifications to each of these procedures for determining least expected cost (where cost is not necessarily travel time) paths and lower bounds on the expected costs of these paths are given. Extensive numerical tests are conducted to illustrate the algorithms' computational performance as well as the properties of the solution.

/pdf/least-expected-time-paths-in-stochastic-time-varying-4jew7epc01.pdf

Elise Miller-Hooks

Papers

A Green Vehicle Routing Problem

Measuring and maximizing resilience of freight transportation networks

Fleet Management for Vehicle Sharing Operations

Resilience: An Indicator of Recovery Capability in Intermodal Freight Transport

Least Expected Time Paths in Stochastic, Time-Varying Transportation Networks