Graduate School of Science, Engineering and Technology, Istanbul Technical University, 34467 Istanbul, Turkey.
Civil Engineering Faculty, Istanbul Technical University, 34467 Istanbul, Turkey.
Sensors (Basel). 2021 Apr 26;21(9):3039. doi: 10.3390/s21093039.
Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users-i.e., users with higher trip rates-are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users' share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16-21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1-6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments.
现有的公共交通 (PT) 规划方法采用基于出行的方法，而不是基于用户的方法，导致忽视了公平性。换句话说，由于较高的出行率，经常使用公共交通的用户——即出行率较高的用户——在分析和建模过程中被过度代表。与现有研究不同，本研究旨在展示日常和月度数据中实际需求特征和用户份额的不同。为此，对土耳其科贾埃利的一个月智能卡数据进行了评估，使用了特定变量，如登乘频率、持卡人类型和用户数量，以及每个用户出行天数的细分。结果表明，在一个工作日，常规 PT 用户在总用户中的比例高于月度常规 PT 用户在总用户中的比例。相应地，登乘频率为 16-21 天的用户占总用户的 16%，但在 1 天的分析中，他们的比例被高估了 39%。此外，登乘频率为 1-6 天的用户在月度数据集中的份额为 66%，而在 1 天的分析中，他们的份额被低估了 22%。结果表明，没有与日常出行频率和公共交通使用相关信息的日出行数据导致了对真实公共交通需求的错误估计。此外，基于用户的一个月分析方法为交通规划、设计和交通投资的优先排序提供了更现实的基础。
