Abstract

In this paper we describe how we included travel time variability in the national Dutch transport forecasting model and what the policy impacts of this new forecasting tool are. Until now, travel time reliability improvements for road projects were included in Dutch cost-benefit analysis (CBA) by multiplying the travel time benefits from reduced congestion by a factor 1.25. This proportionality is based on the linkage between congestion reduction and reliability improvements. However, this treatment of reliability is not useful to evaluate policies that especially affect travel time variability. From the start, this method was provisional and meant to be replaced by a better method capturing travel time variability. For this, we derived an empirical relation between the standard deviation of travel time, mean delay of travel time and length of route. This has been implemented in the national Dutch model as a post processing module. The new travel time reliability forecasting model will be incorporated in the Dutch guidelines for CBA. Document type: Part of book or chapter of book

Abstract

We consider a stochastic routing model in which the goal is to find the optimal route that incorporates a measure of risk The problem arises in traffic engineering, transportation and even more abstract settings such as task planning (where the time to execute tasks is uncertain), etc The stochasticity is specified in terms of arbitrary edge length distributions with given mean and variance values in a graph The objective function is a positive linear combination of the mean and standard deviation of the route Both the nonconvex objective and exponentially sized feasible set of available routes present a challenging optimization problem for which no efficient algorithms are known In this paper we evaluate the practical performance of algorithms and heuristic approaches which show very promising results in terms of both running time and solution accuracy. Document type: Part of book or chapter of book

Abstract

This paper uses a popular web mapping and transportation service to generate information for more than 22 million counterfactual trip instances in 154 large Indian cities. It then develops a methodology to estimate robust indices of mobility for these cities. The estimation allows for an exact decomposition of overall mobility into uncongested mobility and the congestion delays caused by traffic. The paper first documents wide variation in mobility across Indian cities. It then shows that this variation is driven primarily by uncongested mobility. Finally, the paper investigates correlates of mobility and congestion. Denser and more populated cities are slower, in part because of congestion, especially close to their centers. Urban economic development is generally correlated with better uncongested mobility, worse congestion, and overall with better mobility. Document type: Book