Inferring Public Transport Access Distance from Smart Card Registration and Transaction Data

Abstract

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.

Journey-based characterization of multi-modal public transportation networks

“…Assuming that the first journey of the day began at this home address, the registration postcode can be linked to the Oyster card journeys analyzed to infer the true origin location for these journeys. Overall, the access distance distributions inferred using this linkage methodology were found to match well with data from the London Travel Demand Survey (Viggiano et al, 2016). Not all Oyster cards are registered, and some registration postcodes were unrealistically far from the initial boarding stop or station, suggesting the individual either moved or did not begin the first journey from home.…”

Applying Spatial Aggregation Methods to Identify Opportunities for New Bus Services in London

“…The process of developing appropriately sized zones that reflect the structure of the existing public transport system in London is described in detail in Viggiano et al ( 28 ). The result is 1,000 zones (shown in Figure 1) with an average radius of approximately 0.7 km, which corresponds to the average access distance to public transport (bus and rail) stops and stations, and to the 75th percentile of access distances specifically to bus stops in London ( 30 ). By spatially clustering bus stop and rail station locations, the resulting zones have fewer bus stops and rail stations along zonal boundaries, compared with existing London zonal schemes.…”

Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data

“…Therefore, we look for the trajectory points in the travel segment where all trips decelerate, stop, and re-accelerate and find whether there is a bus stop near these point. The value for calculating the ratio of stops near bus stops to all stops is given in the following Equation (2).…”

“…The complete profile of an individual can be represented by the trip chain [1], which should include the travel mode, activity, time, and location of the whole journey. Urban transport management departments have a large amount of transport data for many different modes [2] and can analyze bus and metro trips using smart card data, but they do not have access to the complete public transportation trip chain that includes walking. With the popularity of smart phones and the mass adoption of social software, it has become possible to collect large-scale Internet location data [3,4].…”