Modeling the Taxi Drivers’ Customer-Searching Behaviors outside Downtown Areas

Abstract

A popular phenomenon in the street-hailing taxi system is the imbalanced mobility services between city central and outside downtown areas, which leads to unmet demand outside downtown areas and competitions in city central areas. Understanding taxi drivers' customer-searching behaviors is crucial to addressing the phenomenon and redistributing the taxi supply. However, the current literature ignores or simply models the taxi drivers' behaviors, in particular, lacks the in-depth discussions on individuals' heterogeneity. This study introduces the latent class model to identify the internal and external factors influencing the taxi drivers' destination choice after the last drop-offs. Beyond the influencing factors, the modeling structure captures the heterogeneity in vacant taxicab drivers through introducing latent classes. The proposed model outperforms other discrete choice models, for instance, multinomial logit, nested logit, and mixed logit, based on the two study cases developed from the New York City yellow taxicab system. The empirical results first statistically indicate the existence of latent classes, which further empirically prove the heterogeneity in the choices by vacant taxicab drivers while searching customers. Moreover, we obtain a set of internal and external factors influencing the customer searching behaviors. For example, the taxicab drivers are sensitive to the demand at the search destination areas and the distance from the last drop-off location to the search destination areas and behave identically in particular under the conditions of high demand and short search distance. On the other hand, the external variables have different impacts on customer searching behaviors across the different groups of drivers in the both study cases, including peak hours, weekday, holiday, earned fare from last occupied trip, raining hours, and flight arrivals at airports. In final, the proposed modeling structure and findings are useful as a sub-model of taxi system modeling while developing strategies, as well as as a regional planning tool for taxi supply estimations.

Modeling the decision of ridesourcing drivers to park and wait at trip ends: a comparison between Perth, Australia and Kolkata, India

“…Apart from understanding the factors influencing drivers’ decision on passenger-search, Zhang et al ( 2018 ) also formulated a latent class model using the detailed trip data of 13,000 taxis in New York City to investigate drivers’ behaviour outside downtown areas and found that the destination preference to search passengers varied significantly across the drivers. Such preference for destination may also be prevalent among the ridesourcing drivers since the ridesourcing drivers are assigned with the riders within their proximity.…”

“…Moreover, the research on the significance of spatial locations on drivers’ decision is also quite limited. While Zheng et al ( 2018 ) highlighted the importance of the arrival time of the flights on drivers’ decisions, Zhang et al ( 2018 ) mentioned the difference in customer searching behaviour between inside and outside of central city areas. Both the studies emphasised the influence of spatial locations on drivers’ behaviour.…”

“…Although studies like Zheng et al ( 2018 ) and Zhang et al ( 2018 ) have explored drivers’ tactical and operational decisions in China and United States respectively, study on ridesourcing drivers’ waiting strategy is yet to be explored. The drivers' waiting time tolerance depends on time and location and is also related to their experience, driving behaviour, and socioeconomic characteristics.…”

Spatial Pattern and Driving Mechanism of Urban Taxi Fares in China

“…For instance, W. Zhang et al (2018) built a model to analyze how taxi drivers find customers when they get off the train outside the city center and to determine which internal and external factors influence their preferences (W. Zhang et al, 2018).…”

A Stochastic Simulation Model for the Optimization of the Taxi Management System

“…The distance between leaving a passenger and picking up another one, in other words, the rate of undertaking the next job, was also examined [22]. Empirical considerations of the way drivers look for their next passenger can also be found in the literature [23].…”