Multiroute corridors are a common feature of bus networks. In these corridors, passengers select a route from a set of parallel routes that serve the desired destinations. Understanding how passengers make these decisions can help measure passenger experience and inform network and service planning. A web-based survey was used to collect information on users of a multiroute corridor in London that includes both local and limited-stop bus service. The survey was used both as a tool to understand behavior and as a demonstration case for the viability of web-based surveys, a relatively new methodology for data collection on public transport user behavior. The representativeness and the accuracy of the survey responses were analyzed. The results revealed that online surveys could collect detailed information from a large, fairly representative sample of bus passengers. The responses to questions in the survey were used to categorize passenger behavior by route choice strategy. Passengers could either wait for a bus of a specific route or take the first bus to arrive that serves their destination. The survey data showed that passengers’ route choice strategies were influenced by several factors, including trip length, trip purpose, passenger income, use of countdown next-bus information, passenger attitudes toward crowding, and levels of risk aversion.

Planners must understand how public transportation systems are used in order to make strategic decisions. Smart card transaction data provides vast, detailed records of network usage. Combined with other automatically collected data sources, established inference methodologies can convert smart card transactions into complete linked journeys made by individuals within the public transport network. However, for large, multi-modal public transport networks it can be challenging to summarize the journey records meaningfully. This paper develops a method for categorizing origin-destination (OD) pairs by public transport mode or combination of modes used. By aggregating across OD pairs, this categorization scheme summarizes the multi-modal aspects of public transport network usage. The methodology can also be applied to subsets of data filtered by time of day or geography. The categorization results can inform performance analysis of OD pairs, allowing planners to make comparisons between pairs served by different combinations of modes. London Oyster card data is analyzed to illustrate how the OD pair categorization can characterize a network, allowing planners to quickly assess the roles of different modes, and perform OD pair analysis in a multi-modal public transport network.

Access distance to public transport is an important metric for planning, modeling, and evaluating public transport networks and is often used in policy goals and statements. However, accurately measuring access (and egress) distance can be difficult. Estimates often rely either on aggregate inferences based on census data or on small samples of disaggregate data from travel diary surveys. When smart cards used for fare payment are also registered with home address information, they represent a new data source that can be used to infer access distances for a large sample of users, at a disaggregate level and at low cost, compared with travel diary surveys. This paper demonstrates the inference of access distance from smart card fare and transaction data for a large sample of London public transport journeys and compares the inferred access distributions to data from the London Travel Demand Survey, a travel diary survey. Possible instances of false inferences are considered and measures to eliminate false inferences are discussed. This access distance inference methodology allows for the analysis of variation in access distance across the network, and examples of this type of analysis are presented.

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Innovative analyses of origin-destination (OD) data derived from automatic fare collection and automatic vehicle location systems in public transport networks enable planners to gain new insights into how passengers travel in the network and the quality of service provided, and can even inform decisions about network improvements. Particularly in large, complex networks, systematic, data-driven approaches to network evaluation and planning are essential. New methodologies are needed to transform OD data into informative metrics and planning recommendations. This paper proposes a framework for this process and applies it to London's public transport network. Though there are many ways to improve public transport networks, this paper focuses on the addition of new bus routes to reduce circuity. The proposed framework includes three steps that combine OD-level analysis with spatial aggregation methodologies for the identification of corridors for new bus services. First, bus stops and rail stations were clustered into geographic zones. Second, a subset of zonal OD pairs with circuitous service were identified as candidates for improvement through new bus routes, based on performance standards established with user-defined parameters. Third, an algorithm that clusters OD pairs into corridors was applied to identify promising corridors for new bus services. This paper discusses corridors identified for new services in the London case study.