Zhan Zhao

Measuring Regularity of Individual Travel Patterns

Regularity is an important property of individual travel behavior, and the ability to measure it enables advances in behavior modeling, mobility prediction, and customer analytics. In this paper, we propose a methodology to measure travel behavior regularity based on the order in which trips or activities are organized. We represent individuals’ travel over multiple days as sequences of “travel events”—discrete and repeatable behavior units explicitly defined based on the research question and the available data. We then present a metric of regularity based on entropy rate, which is sensitive to both the frequency of travel events and the order in which they occur. The methodology is demonstrated using a large sample of pseudonymised transit smart card transaction records from London, U.K. The entropy rate is estimated with a procedure based on the Burrows-Wheeler transform. The results confirm that the order of travel events is an essential component of regularity in travel behavior. They also demonstrate that the proposed measure of regularity captures both conventional patterns and atypical routine patterns that are regular but not matched to the 9-to-5 working day or working week. Unlike existing measures of regularity, our approach is agnostic to calendar definitions and makes no assumptions regarding periodicity of travel behavior. The proposed methodology is flexible and can be adapted to study other aspects of individual mobility using different data sources.

Automatic Data for Applied Railway Management: Case Study on the London Overground

In 2009, London Overground management implemented a new tactical plan for a.m. and p.m. peak service on the North London Line (NLL). This paper documents that tactical planning intervention and evaluates its outcomes in terms of certain aspects of service delivery (the operators perspective on system performance) and service quality (the passengers perspective). Analyses of service delivery and quality and of passenger demand contributed to the development, proposal, and implementation of the new tactical plan. NLL trains were delayed routinely en route, with excessive dwell time a major cause. Near-random passenger incidence behavior suggested that an even headway service for NLL might have been more appropriate. The confluence of these analyses was confirmed by the corresponding excess journey time results. On the basis of longitudinal analysis, an evaluation showed that on-time performance increased substantially and observed journey time decreased with the introduction of the new plan. Overall, the effects of this implementation appeared to have been positive on balance. This case study thus demonstrated the applicability of automatic data generally, and certain measures and techniques in the London Overground specifically, to support the tactical planning of an urban railway.

Has Transportation Demand of Shanghai, China, Passed Its Peak Growth?

On the basis of four comprehensive transportation surveys in Shanghai, China, this study examined the latest trends in Shanghais travel demand; investigated their social, economic, and spatial drivers; and compared the pace of travel demand growth in three periods: 1980s to early 1990s, early 1990s to mid-2000s, and mid-2000s to the present. The demand growth was relatively slow in the first period and then sped up in the second before it returned to a slower pace in the third period. As for trip purpose, Shanghais travel is much more diversified than previously, with an increasing share of noncommuting trips (from 28% in 1995 to 46% in 2009). Spatially, travel demand is dispersed from the central district to peripheral districts because of urban expansion and decentralization and from Puxi (west of the Huangpu River) to Pudong (east of the Huangpu River) as a result of significant economic development of the Pudong New Area. Both spatial diffusion and purpose diversification favor the convenience and flexibility of private motor vehicles. Driven by rapid motorization, vehicle travel is growing at a much faster pace than person travel. Overall, the annual growth rate for travel demand in Shanghai reached its peak in 2004 for both person trips and vehicle trips. In absolute numbers, person trip growth has peaked, but vehicle trip growth has not. In response to the growing demand, especially rapid motorization, the local government has made tremendous investments in road infrastructure and public transit, and it has attempted to manage demand through vehicle ownership control.

Understanding car pride

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