Developing a comprehensive Bus Ridership Data Analysis System would allow NJ TRANSIT to better anticipate travel demand, more effectively ensure that all communities are provided equitable service, and better target its customer outreach efforts. Currently, NJ TRANSIT uses fare revenue as an indicator of passenger ridership. The agency also relies on passenger surveys to obtain samples of ridership demographics as well as passenger trip origins and destinations. With the introduction of NJ TRANSIT's Smart Bus program, a parallel dataset has become available providing more precise information on bus passenger boardings and deboardings as well as trip run times. NJ TRANSIT believes that with proper quality control and statistical analysis, the smart bus dataset could be combined with the passenger survey data to power a data analysis application for improving demand forecasting, Title VI Civil Rights compliance efforts, and customer outreach.
The Smart Bus data are collected daily for each bus that runs revenue trips. Survey teams regularly collect passenger background information onboard buses from samples of the ridership population. With proper statistical weighting, the passenger survey data could complement the Smart Bus Data, allowing inferences to made about passenger origins and destinations, demographics, and other background information. This combined dataset would power a Bus Ridership Analysis System that would serve NJ TRANSIT departments such as Bus Service Planning, Civil Rights & Diversity, Travel Demand Forecasting & Research, Market Research, and GIS.
The selected consultant will utilize origin-destination travel surveys on specified bus routes that was collected over the last few years or new survey data that will be collected separately from this project in 2019. All bus origin-destination data will be provided to the consultant by NJ TRANSIT.
The consultant will explore the Passenger Survey and the Smart Bus datasets to identify how they can be combined to produce reliable inferences, ultimately yielding a product dataset that can be queried to provide estimates of ridership volumes, origins, destinations, , and demographic breakdowns. Such data would potentially be available at the route, operating direction, and farezone levels allowing for both macro and micro ridership analyses.