Hoboken Terminal: Using Google Maps API to Optimize Transit Accessibility and Mobility

As part of a larger transit project, ESI recently evaluated travel times to job centers in New York City from all transit stops in Hudson County on bus, light rail, and PATH lines with direct access to Hoboken Terminal. The purpose of the analysis was to identify routes and stops that could be optimized to increase ridership at Hoboken Terminal and improve travel times for transit users. Our approach was to identify where traveling via Hoboken Terminal to New York has a comparative time-savings advantage vis-a-vis other possible routes or modes of transit as well as identify areas where, if service were improved, Hoboken Terminal might gain a comparative advantage and therefore attract more riders. To complete this analysis, we used Google Maps API. 

Google Maps API is an application programming interface (API) that allows users to access the extensive data behind Google Maps without having to use their website. For this project, we used the package gmapsdistance in R to utilize the feature of Google Maps API that calculates travel times across various modes of transit between two points. Compared to traditional transportation planning/engineering software that primarily focuses on the network design and traffic demand modeling, Google Maps API particularly allocates precise predicted travel times by modes over multiple origins and destinations (ODs) at one time. It allows planners to evaluate transportation accessibility and mobility by modes of an entire region. 

Our analysis took three steps: coordinate identification, travel time estimation, and visualization. First, we extracted the coordinates of all transit stops served by transit lines with direct connections to Hoboken Terminal as well as the coordinates of three job centers in Manhattan closest to New Jersey: Hudson Yards in Midtown, the downtown Financial District, and the emerging, yet considerably smaller, employment center at Hudson Square.

Next, using Google Maps API, we generated travel times from each transit stop to each employment center along three different modes/routes: 1) driving along the optimal route as identified by Google Maps, 2) public transit along the optimal route, and 3) public transit via Hoboken Terminal.   

Finally, we visualized and identified areas of improvement by comparing the travel time difference bands between travel modes and route choices. By mapping those stops symbolized by their time difference bands compared to default transit, we were able to categorize the travel time difference  numeric into three categories:  

A. Direct transit is only 0-10 minutes faster than taking transit through Hoboken Terminal  

B. Direct transit could save more than 10 minutes than transit through Hoboken Terminal  

C. Transit through Hoboken Terminal is faster than Direct transit.  

The literatures has suggested that time savings in transportation could have created extra benefits that can be monetized. For intermodal terminals like Hoboken, optimizing the schedules across different modes that reduces the connection waiting time is the most direct and significant way to eliminate the marginal difference in travel times and boost terminal ridership. Bus stops with marginal difference bands of A) and B) are the area of our focus, to expand the catchment area for the Hoboken Terminal – transportation riders working/living in these areas are more likely to switch to services via Hoboken Terminal once the time cost would be further reduced and the service to Hoboken is improved.  

Tveter, Eivind. “The value of travel time: a revealed preferences approach using exogenous variation in travel costs and automatic traffic count data.” Transportation (2022): 1-25. 

Bruno, Giuseppe, Gennaro Improta, and Antonino Sgalambro. “Models for the schedule optimization problem at a public transit terminal.” OR spectrum 31 (2009): 465-481. 


Lechuan Huang | [email protected]

Lechuan Huang is an Analyst at ESI. He has an educational background in urban planning, receiving his Bachelor of Arts in Public and Urban Affairs from Virginia Tech, and his Master of Urban Spatial Analytics from the University of Pennsylvania.

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