Abstract
Trip generation modeling is essential in transportation planning activities. Previous modeling methods that depend on traditional data collection methods are inefficient and expensive. This paper proposed a novel data-driven trip generation modeling method for urban residents and non-local travelers utilizing location-based social network (LBSN) data and cellular phone data and conducted a case study in Nanjing, China. First, the point of interest (POI) data of the LBSN were classified into various categories by the service type, then, four features of each category including the number of users, number of POIs, number of check-ins, and number of photos were aggregated by traffic analysis zones to be used as explanatory variables for the trip generation models. We used a random tree regression method to select the most important features as the model inputs, and the trip models were established based on the ordinary least square model. Then, an exploratory approach was used to test the performance of each combination of the variables with various test methods to identify the best model for residents’ and travelers’ trip generation functions. The results suggest land use compositions have significant impact on trip generations, and the trip generation patterns are different between urban residents and non-local travelers.
Highlights
Trip generation modeling is the first step in the traditional travel demand forecasting procedure, and it is integral in evaluating the transportation impacts of land use developments
Weibo provides an application programming interface (API) [21] for collecting detailed information at each point of interest (POI), including the locations, the service types, and statistics about the total checkin counts, user counts, and number of photos uploaded by users
Weibo provides an application programming interface (API) [21] for collecting detailed information at each POI, including the locations, the service types, and statistics about the total check-in counts, user counts, and number of photos uploaded by users
Summary
Trip generation modeling is the first step in the traditional travel demand forecasting procedure, and it is integral in evaluating the transportation impacts of land use developments. The LBSN data, which includes the POI type and check-in number, could be used as variables suggesting land use and attractiveness of trips. For future trip generation estimation with new land use developments, reasonable prediction of trip production/attraction of urban residents and non-local travelers could be calculated using the LBSN data. This paper aimed to propose a novel trip generation modeling method using LBSN data and cellular phone data for both residents and travelers in urban cities. Weibo provides an application programming interface (API) [21] for collecting detailed information at each POI, including the locations, the service types, and statistics about the total check-in counts, user counts, and number of photos uploaded by users. SSttaattiissttiiccss ooff tthhee PPOOII ddiissttrriibbuuttiioonnss:: ((aa)) SShhaarree ooff PPOOIIss ppeerr CCaatteeggoorryy;; ((bb)) AAvveerraaggee CChheecckkiinnss ppeerr PPOOII;; ((cc)) AAvveerraaggeePPhhoottoossppeerrPPOOII;;((dd))AAvveeraraggeeUUsseersrsppeerrPPOOI.I
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