2018

NJ TRANSIT customers can use a smartphone application (“app”) to purchase tickets and access transit information.  Most smartphones are equipped with technology that can determine the user’s location; however, this feature is currently used in a limited capacity in NJ TRANSIT’s app.  By knowing a customer’s location, the transit agency could potentially provide customized information directly to passengers based on their location, which is referred to as geotargeting.  The objective of this research project is to assess NJ TRANSIT passengers’ receptiveness to geotargeting in NJ TRANSIT’s mobile app.  The methodology was an online survey in which more than one thousand NJ TRANSIT passengers participated.  The results of the survey reveal that most app users understand that their smartphone can detect their location, and most respondents find it acceptable for NJ TRANSIT’s app to detect their location.  After providing specific examples of potential geotargeted features in NJ TRANSIT’s app to survey respondents, the most desired feature was targeted transit service alerts.  Examples of targeted coupons and advertising were also presented to survey respondents; however, these received mixed feedback from participants.  In summary, the results suggest that NJ TRANSIT passengers find it acceptable for NJ TRANSIT’s app to know their location, and they are particularly receptive to receiving targeted transit information relevant to their transit trip.

In most cities, public transport users are far from consistent in their use of the service over time. With time, many people leave the system or change their level of use. In this paper, passive data from two public transport systems are used to analyze the migration of passengers to other modes and their changes in the level of public transport usage. In both cases, we use large datasets obtained from smart card data to develop an ordered logistic model that identifies the attributes of the system that play an important role in explaining the level of usage of the system. Three categories of level of usage are defined and used as the dependent variable. Migration (leaving the system) is added to try to explain the phenomenon. Meaningful results are found for the Santiago, Chile case, and compared with the results found for Gatineau, Quebec, Canada. Factors like the frequency of use, the age in the system, and travel time influence the migration rate.

The aim of this study is twofold: 1. to describe the procedure of matching longitudinal smartphone based travel survey data with open operational data, and 2. to quantify the effect of both types of data on customers satisfaction with using rail-based public transport modes. The travel data utilized in this paper originate from the smartphone-based London Mobility Survey (LMS) and was collected between November 2016 to February 2017. The open data matched to the LMS data has been derived from the open TfL API. An ordered logit model is developed to quantify the effect of public transport service status, and individuals’ socio-demographic and trip characteristics on satisfaction with using public transport mode for each one of their trip-stages. Our results indicate that customer satisfaction is indeed associated with the open public transport status data and that satisfaction depends on each trip and the conditions of the trip. Activities while travelling and trip purpose also affect customers satisfactions, while these results provide insights for offering products that can advance customers experience in the Mobility-as-a-Service and automated vehicles era that lies ahead.

This study applies high-resolution GPS bus transit data to study the effect of route and roadway changes utilizing detailed data collected before and after the completion of a major transit project. A new methodology is presented to compare percentile and time-of-day performance measures before and after the project. Additionally, differences in travel time and travel time variability are examined. The case study examines a major bus route in Portland that was recently diverted onto a newly built transit only bridge. The results show that the expected travel time reductions did not take place. Travel times increased for the majority of trips but travel time variability during the peak period was sharply reduced.