Mining Connected Vehicle Data for Beneficial Patterns in Dubai Taxi Operations

Abstract

On-demand shared mobility services such as Uber and microtransit are steadily penetrating the worldwide market for traditional dispatched taxi services. Hence, taxi companies are seeking ways to compete. This study mined large-scale mobility data from connected taxis to discover beneficial patterns that may inform strategies to improve dispatch taxi business. It is not practical to manually clean and filter large-scale mobility data that contains GPS information. Therefore, this research contributes and demonstrates an automated method of data cleaning and filtering that is suitable for such types of datasets. The cleaning method defines three filter variables and applies a layered statistical filtering technique to eliminate outlier records that do not contribute to distributions that match expected theoretical distributions of the variables. Chi-squared statistical tests evaluate the quality of the cleaned data by comparing the distribution of the three variables with their expected distributions. The overall cleaning method removed approximately 5% of the data, which consisted of errors that were obvious and others that were poor quality outliers. Subsequently, mining the cleaned data revealed that trip production in Dubai peaks for the case when only the same two drivers operate the same taxi. This finding would not have been possible without access to proprietary data that contains unique identifiers for both drivers and taxis. Datasets that identify individual drivers are not publicly available.

Research on the Choice Behavior of Taxis and Express Services Based on the SEM-Logit Model

“…Unlike taxis, online car-hailing services can only carry passengers through network appointments and are not allowed to cruise (Service types of them are showed in Figure 1). Although both online car-hailing services and taxis can provide personalized and door-to-door services (both of them are on-demand mobility services, and taxis additionally can be used for street hailing [1]), some researchers have shown that there are certain differences in the travel characteristics between them. Cui et al [2] analyzed the online order data of taxis and express services (one type of online car-hailing service; the service and the price are roughly equal to a taxi) and found that, in terms of the travel time of taxi online orders, trips of less than 20 minutes account for 22.8% of all rides, trips of 20-30 minutes account for 24.5%, and trips of 30-40 minutes account for 21.2%.…”

Review of Usage of Real-World Connected Vehicle Data

“…Wali et al ( 26 ) presented data integration and the process steps in a data flow chart. Bridgelall et al ( 38 ) demonstrated an automated method of cleaning the data of a taxi probe dataset that utilized known distributions of vehicle operations to detect possible outliers for removal. Table 5 summarizes the shortcomings of the data and provides recommendations for data cleaning based on knowledge synthesized from the literature search.…”

Adaptive Gaussian mixture model for identifying outliers in historical route travel times

“…Under the assumption that the central part of the TTD follows a well‐known distribution, Bridgelall et al. [30] proposed an outlier‐identification approach to remove outliers that moved the distributions further away from the known theoretical distributions, and chi‐squared statistical tests were used to validate the quality of the results. Park and Kim [31] proposed a similar approach of filtering the outliers repetitively in the travel time data, resulting in the remaining data resembling the normal distribution.…”