Benefit-Cost Analysis for Transportation Planning and Public Policy: Towards Multimodal Demand Modeling

This report examines existing methods of benefit-cost analysis (BCA) in two areas, transportation policy and transportation planning, and suggests ways of modifying these methods to account for travel within a multimodal system. Although the planning and policy contexts differ substantially, this report shows how important multimodal impacts can be incorporated into both by using basic econometric techniques and even simpler rule-of-thumb methods. Case studies in transportation planning focus on the California Department of Transportation (Caltrans), but benchmark California’s competencies by exploring methods used by other states and local governments. The report concludes with a list and discussion of recommendations for improving transportation planning models and methods. These will have immediate use to decision makers at Caltrans and other state DOTs as they consider directions for developing new planning capabilities. This project also identifies areas, and lays groundwork, for future research. Finally, by fitting the planning models into the broader context of transportation policy, this report will serve as a resource for students and others who wish to better understand BCA and its use in practice

Roadmap to Gridlock: The Failure of Long-Range Metropolitan Transportation Planning

Federal law requires metropolitan planning organizations in urban areas of more than 50,000 people to write long-range (20- to 30- year) metropolitan transportation plans and to revise or update those plans every 4 to 5 years. A review of plans for more than 75 of the nation's largest metropolitan areas reveals that virtually all of them fail to follow standard planning methods. As a result, taxpayers and travelers have little assurance that the plans make effective use of available resources to reduce congestion, maximize mobility, and provide safe transportation facilities. Nearly half the plans reviewed here are not cost effective in meeting transportation goals. These plans rely heavily on behavioral tools such as land-use regulation, subsidies to dense or mixed-use developments, and construction of expensive rail transit lines. Nearly 40 years of experience with such tools has shown that they are expensive but provide negligible transportation benefits. Long-range transportation planning necessarily depends on uncertain forecasts. Planners also set qualitative goals such as "vibrant communities" and quantifiable but incomparable goals such as "protecting historic resources." Such vagaries result in a politicized process that cannot hope to find the most effective transportation solutions. Thus, long-range planning has contributed to, rather than prevented, the hextupling of congestion American urban areas have suffered since 1982. Ideally, the federal government should not be in the business of funding local transportation and dictating local transportation policies. At the least, Congress should repeal long-range transportation planning requirements in the next reauthorization of federal surface transportation funding. Instead, metropolitan transportation organizations should focus planning on the short term (5 years), and concentrate on quantifiable factors that are directly related to transportation, including safety and congestion relief

Route Planning in Transportation Networks

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4, previously published by Microsoft Research. This work was mostly done while the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at Microsoft Research Silicon Valle

Paving The Way: Recruiting Students into the Transportation Professions, MTI Report 08-03

The transportation industry faces a growing shortage of professional engineers and planners. One key strategy in solving this problem will be to encourage more civil engineering and urban planning students to specialize in transportation while completing their degrees, so that employers have a larger pool of likely recruits. However, very little is known about how these students choose a specialization. To help fill that gap, this report examines the factors that lead civil engineering undergraduates and urban planning masters students to specialize in transportation, as opposed to other sub-disciplines within the two fields. The primary data collection methods were web-based surveys of 1,852 civil engineering undergraduates and 869 planning masters students. The study results suggest steps the transportation community can take to increase the number of civil engineering and planning students who choose to specialize in transportation

A Windowed Transportation Planning Model

This research develops and applies a transportation planning model that integrates regional and local area forecasting approaches. While regional models have the scope to model the interaction of demand and congestion, they lack the spatial detail of a local approach. Local approaches typically do not consider the feedback between new project traffic and existing levels of traffic. Using a window, which retains the regional trip distribution information and the consistency between travel demand and congestion, allows the use of a complete transportation network and block level traffic zones while retaining computational feasibility. By combining the two methods, a number of important policy issues can be addressed, including the implications of traffic calming, changes in flow due to alternative traffic operation schemes, the influence of micro-scale zoning changes on nearby intersections, the impact of TDM on traffic congestion, and the consequences of a suburban light rail line.transportation planning model, traffic impact study, travel demand model, intersection control, window .

The crew-scheduling module in the GIST system

The public transportation is gaining importance every year basically due the population growth, environmental policies and, route and street congestion. Too able an efficient management of all the resources related to public transportation, several techniques from different areas are being applied and several projects in Transportation Planning Systems, in different countries, are being developed. In this work, we present the GIST Planning Transportation Systems, a Portuguese project involving two universities and six public transportation companies. We describe in detail one of the most relevant modules of this project, the crew-scheduling module. The crew-scheduling module is based on the application of meta-heuristics, in particular GRASP, tabu search and genetic algorithm to solve the bus-driver-scheduling problem. The metaheuristics have been successfully incorporated in the GIST Planning Transportation Systems and are actually used by several companies.Integrated transportation systems, crew scheduling, metaheuristics

An Optimization Model for Single-Warehouse Multi-Agents Distribution Network Problems under Varying of Transportation Facilities: A Case Study

The transportation cost of goods is the highest day-to-day operational cost associated with the food industry sector. A company may be able to reduce logistics cost and simultaneously improve service level by optimizing of distribution network. In reality, a company faces problems considering capacitated transportation facilities and time constraint of delivery. In this paper, we develop a new model of order fulfillment physical distribution to minimize transportation cost under limited of transportation facilities. The first step is defined problem description. After that, we formulate a integer linear programming model for the single-warehouse, multiple-agents considering varying of transportation facilities in multi-period shipment planning. We analyze problems faced by company when should decide policy of distribution due to varying of transportation facilities in volume, type of vehicle, delivery cost, lead time and ownership of facilities. We assumed transportation costs are modeled with a linear term in the objective function. Then, we solve the model with Microsoft Excel Solver 8.0 Version. Finally, we analyze the results with considering amount of transportation facilities, volume usage and total transportation cost. Keywords: physical distribution, shipment planning, integer linear programming, transportation cost, transportation facilities

CITIES AND ACCESSIBILITY: THE POTENTIAL FOR CARBON REDUCTIONS AND THE NEED FOR NATIONAL LEADERSHIP

This article begins by outlining the elements that should be included in the framework for understanding how people interact with their built environments. Part II describes how the framework might be made operational through the use of an emerging technique called land-use transportation scenario planning. Part III assesses how well land-use transportation scenario planning fits within the dictates and limits of U.S. transportation law. The analysis ultimately reveals that it holds substantial promise as a tool that could lead to meaningful cuts in carbon emissions