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Data-driven planning of reliable itineraries in multi-modal transit networks

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Abstract

Multi-modal travel itineraries are based on traversing multiple legs using more than one mode of transportation. The more combinations of legs and modes, the more challenging it is for a traveler to identify a reliable itinerary. Transportation providers collect data that can increase transparency for reliable travel planning. However, this data has not been fully exploited yet, although it will likely form an important piece of future traveler information systems. Our paper takes an important step in this direction by analyzing and aggregating data from the operation of scheduled and unscheduled modes to create a reliability measure for multi-modal travel. We use a network search algorithm to evaluate itineraries that combine schedule-based long-distance travel with airlines with last-mile and first-mile drive times to efficiently identify the one with the highest reliability given a start time and travel-time budget. Our network search considers multiple origin and destination airports which impacts the first and last mile as well as the flight options. We use extensive historical datasets to create reliable itineraries and compare these with deterministic shortest travel-time itineraries. We investigate the amount of data that is required to create reliable multi-modal travel itineraries. Additionally, we highlight the benefits and costs of reliable travel itineraries and analyze their structure.

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Authors and Affiliations

  1. Department of Business Analytics, University of Iowa, 108 John Pappajohn Business Building, Iowa, IA, 52242, USA

    Michael Redmond & Ann Melissa Campbell

  2. Management Science Group, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Jan Fabian Ehmke

Authors
  1. Michael Redmond
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  2. Ann Melissa Campbell
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  3. Jan Fabian Ehmke
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Correspondence to Michael Redmond.

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Appendix

Appendix

1.1 OD pairs

Table 9 examines key statistics from each of the OD pairs considered in the experiments from Sect. 6. SP #_Flights and MRI #_Flights is the average number of flights on itineraries between these cities. For example, New York to Los Angeles always chooses direct flights while Bozeman, MT to Panama City, FL averages more than 3 flights. The reliability for each stage of the SP and MRI itineraries are also displayed. This OD summary table can help trace which cities have reliability loss at different stages and how to use this to plan more reliable itineraries.

Table 9 30 OD pairs summary table
Full size table

1.2 K–S test statistic

Figure 11 is similar to the analysis in Sect. 5.1. We compared the predicted and actual distributions using the two-sample Kolmogorov–Smirnov (K–S) Test Statistic (Smirnov 1948) to evaluate similarity between distributions. A value closer to 0 means the distributions are more similar, while a value close to 1 signifies greater differences. As Fig. 11 shows, when comparing predicted and actual distributions over 4 months of data (April, July, October, December), the average K–S statistic tends to drop the more historical observations are present. The larger k-statistic with observations that only have a small number of distributions shows that the predicted and actual distributions for a small number of observations is not as predictive. As shown in Fig. 11, at around 15–25 observations, the K–S statistic levels out.

Fig. 11
figure 11

Comparison of effect of number of observations on average K–S statistic

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Redmond, M., Campbell, A.M. & Ehmke, J.F. Data-driven planning of reliable itineraries in multi-modal transit networks. Public Transp 12, 171–205 (2020). https://doi.org/10.1007/s12469-019-00221-0

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