Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets

Abstract

Smart transportation is an important part of smart urban areas, and travel characteristics analysis and traffic prediction modeling are the two key technical measures of building smart transportation systems. Although online car-hailing has developed rapidly and has a large number of users, most of the studies on travel characteristics do not focus on online car-hailing, but instead on taxis, buses, metros, and other traditional means of transportation. The traditional univariate variable hybrid time series traffic prediction model based on the autoregressive integrated moving average (ARIMA) ignores other explanatory variables. To fill the research gap on online car-hailing travel characteristics analysis and overcome the shortcomings of the univariate variable hybrid time series traffic prediction model based on ARIMA, based on online car-hailing operational data sets, we analyzed the online car-hailing travel characteristics from multiple dimensions, such as district, time, traffic jams, weather, air quality, and temperature. A traffic prediction method suitable for multivariate variables hybrid time series modeling is proposed in this paper, which uses the maximal information coefficient (MIC) to perform feature selection, and fuses autoregressive integrated moving average with explanatory variable (ARIMAX) and long short-term memory (LSTM) for data regression. The effectiveness of the proposed multivariate variables hybrid time series traffic prediction model was verified on the online car-hailing operational data sets.

A Short-Term Hybrid TCN-GRU Prediction Model of Bike-Sharing Demand Based on Travel Characteristics Mining

“…As an important means of transportation for urban residents, bike-sharing often presents different characteristics due to a variety of factors, which must be explored for the purpose of traffic management. Therefore, based on the considered dataset, we explored bike-sharing travel characteristics via several dimensions such as time, temperature, humidity, and weather [ 33 ].…”

Modeling the effects of the governmental responses to COVID-19 on transit demand: The case of Athens, Greece

“…These include recently published works on passenger demand forecasting with seasonal ARIMAX models ( Gong et al, 2014 , Tao et al, 2018 , Virati et al, 2020 ), metro ridership with dual ARIMA-GARCH models Ding et. al (2017), online car-hailing services demand forecasting with ARIMAX models ( Zhou et al 2021 ), and hybrid ARIMA-neural network models for bus demand prediction ( Chiang et al 2011 ). A closer look at these applications shows that, with the exception of the last paper (which is somewhat less recent), in the rest of the papers demand is proxied by validation data from smart cards.…”

Analyzing the multi‐scale characteristic for online car‐hailing traffic volume with quantum walk

“…As an emerging travel mode, online car-hailing, its spatial distribution dynamics makes the online car-hailing traffic volume have significant spatiotemporal heterogeneity, which leads to the complex multi-scale characteristics of online car-hailing traffic volume [5,6]. At present, the characteristic analysis of traffic volume is one of the key measures to build an intelligent transportation system [7][8][9]. In addition, various information, such as vehicle trajectory, traffic volume, and so on, generated during the operation of online car-hailing are indicators to measure the potential laws of the spatio-temporal characteristic of urban traffic [10][11][12].…”