Analysis on Difference between Supply and Demand of Urban Taxi Passenger in the Case of Carpooling

Abstract

Urban passenger statistics is an important part of the social economic statistics, as an important part of urban passenger, the difference between supply and demand of taxi will directly impact the quality of residents' travel. The taxi passenger demand is forecasted according to the peoples travel conditions. The taximeter data survey method based on a fixed sample sampling and supplemented by the carpooling survey is put forward to determine the supply of taxi passenger. The difference between supply and demand of taxi passenger is analyzed. Accordingly, the result of the case analysis shows that analysis between supply and demand of taxi passenger provides strong support for government urban passenger management and control and be of great significance on theory and practice to the development of urban passenger transport industry.

Методи Рішення Задач З Організації Пасажирських Перевезень Автомобільним Транспортом

“…Використання рекурентних нейронних мереж було викладено в роботах [32][33]. Запровадження даного методу математичного моделювання надало можливість авторам [33] висунути припущення з моделювання кількісних параметрів потоку пасажирів в часі у вигляді динамічного числа Yt, де t[0;Т] і наведене у функції 7:…”

Analyses of the Imbalance of Urban Taxis’ High-Quality Customers Based on Didi Trajectory Data

“…In recent years, with the development of the global positioning system (GPS) and technology for analyzing big data, it is becoming possible to use GPS real-time positional data of taxis to study related problems. Previous research was mainly concerned with (a) using trajectory data to obtain traffic real-time status [13][14][15], passenger travel status [16,17], and personnel real-time flow [18,19] to either predict urban road congestion [20] or provide reference data for urban traffic planning; (b) exploring the spatial-temporal distribution characteristics related to traffic, such as the spatial-temporal distribution of traffic travel supply and demand [21][22][23][24][25], the spatial-temporal distribution of taxis and passengers [26], the spatial-temporal distribution of the stream of people and traffic flow [27][28][29][30], and occupational and residential traffic [31], by studying the statistical rules of various elements of transportation and providing either theoretical or technical support for traffic management; and (c) the reality of taking advantage of trajectory data to study the efficiency of taxi operation and drivers' service profit margins. Examples are exploring the balance between urban taxi supply and passenger travel to evaluate the operational efficiency based on the capacity utilization data and vacancy data of taxis [32,33], studying the search behavior of taxis in vacancy and simulating the effects of supply and demand balance for taxis [34], establishing taxi and ride-hailing service models to quantify drivers' service profit margins to improve taxi drivers' revenue [5], and constructing a spatial-temporal trajectory model of the balanced distribution with load to optimize the information matching between passengers and drivers and improve the operational efficiency of taxis [35][36][37].…”