Takahiko Kusakabe

Estimation of behavioural change of railway passengers using smart card data

Smart card systems are becoming increasingly popular on a global scale, not just for purchasing general goods and services, but also for paying public transport fares. When a traveller uses a public transport smart card, the exact time of their passage through ticket gates are recorded in the smart card system database. However, these data have not yet been sufficiently studied in the field of transport research. The aims of this paper are to estimate the behaviour of railway passengers by using smart card data and to evaluate the effects of train operations. In particular, the analysis is focused on the comparison of passengers’ travel choice behaviour before and after the railway company altered the train timetable.This paper describes how the passing times of individual passengers at entrance and exit ticket gates are aggregated for a small discrete time interval. Analysis of the departure, travel, and arrival time distributions shows that passengers smoothly adjusted their travel behaviour to the new train timetable. Analysis of the passing times at origin and destination station ticket gates in combination with the train timetable makes it possible to identify which train each traveller was likely to have boarded. This paper also proposes a method to assign a passenger to a combination of trains between an origin and destination stations. The method is examined using actual smart card data.

Traffic state estimation on highway: A comprehensive survey

Abstract Traffic state estimation (TSE) refers to the process of the inference of traffic state variables (i.e., flow, density, speed and other equivalent variables) on road segments using partially observed traffic data. It is a key component of traffic control and operations, because traffic variables are measured not everywhere due to technological and financial limitations, and their measurement is noisy. Therefore, numerous studies have proposed TSE methods relying on various approaches, traffic flow models, and input data. In this review article, we conduct a survey of highway TSE methods, a topic which has gained great attention in the recent decades. We characterize existing TSE methods based on three fundamental elements: estimation approach, traffic flow model, and input data. Estimation approach encompasses methods that estimate the traffic state, based on partial observation and a priori knowledge (assumptions) on traffic dynamics. Estimation approaches can be roughly classified into three according to their dependency on a priori knowledge and empirical data: model-driven, data-driven, and streaming-data-driven. A traffic flow model usually means a physics-based mathematical model representing traffic dynamics, with various solution methods. Input data can be characterized by using three different properties: collection method (stationary or mobile), data representation (disaggregated or aggregated), and temporal condition (real-time or historical). Based on our proposed characterization, we present the current state of TSE research and proposed future research directions. Some of the findings of this article are summarized as follows. We present model-driven approaches commonly used. We summarize the recent usage of detailed disaggregated mobile data for the purpose of TSE. The use of these models and data will raise a challenging problem due to the fact that conventional macroscopic models are not always consistent with detailed disaggregated data. Therefore, we show two possibilities in order to solve this problem: improvement of theoretical models, and the use of data-driven or streaming-data-driven approaches, which recent studies have begun to consider. Another open problem is explicit consideration of traffic demand and route-choice in a large-scale network; for this problem, emerging data sources and machine learning would be useful.

Traffic State Estimation with the Advanced Probe Vehicles Using Data Assimilation

This paper proposes a method for estimating traffic state from data collected by the advanced probe vehicles, namely, probe vehicles with spacing measurement equipment. The probe vehicle data are assumed to include spacing information, in addition to conventional position information. The spacing information is collected as secondary products from advanced vehicle technologies, such as automated vehicles. Traffic states and a fundamental diagram are derived from the probe vehicle data. Then, a traffic state estimator based on a data assimilation technique and a traffic flow model is formulated. This procedure is intended to mitigate negative effects in traffic state estimation caused by high fluctuations in microscopic vehicular traffic. The validation results with a simulation experiment suggested that the proposed method works reasonably, for example, the proposed method was able to estimate precise traffic state compared with the previous methods. Therefore, we expect that the proposed method can estimate precise traffic states in wide area where the advanced probe vehicles are penetrated, without depending on fixed sensor infrastructures nor careful parameter calibration.

Data Mining for Traffic Flow Analysis: Visualization Approach

Data mining has attracted considerable attention as a method that can be used to discover certain characteristics from large amounts of data. In traffic flow analysis, a large amount of traffic flow data is continuously collected and stored over several years.

Evolution of a dynamic ridesharing system based on rational behavior of users

Abstract A dynamic ridesharing system (DRS) is a system where users can find ridesharing partner(s) at any time, even shortly before making a trip. A DRS that does not consider individual preferences may cause dissatisfied matchings of users in a shared vehicle and lead to abandonment of DRS in the long term. To investigate the evolution of DRS, such as long-term adoption, this study develops a model of DRS considering the rational behavior and learning process of its users. User behavior is considered as travel mode choice and ridesharing partner choice decisions under the expected utility maximization concept. The day-to-day evolution of a DRS is simulated based on the proposed model, and the effects of user learning behaviors and some social factors pertinent to long-term DRS adoption are investigated.

Pareto-improving social optimal pricing schemes based on bottleneck permits for managing congestion at a merging section

ABSTRACT Akamatsu, Sato, and Nguyen (2006) proposed a first-best pricing scheme based on the concept of bottleneck permits. The scheme allows permit holders to pass a bottleneck at specified times and is shown to be able to minimize social cost. However, the scheme is not always Pareto-improving in that it may harm some drivers. The objective of this study is to design Pareto-improving pricing scheme with bottleneck permits for a V-shaped two-to-one merge bottleneck. First, the paper formulates the morning commute model in the network and describes the arrival time choice equilibrium in the network with merging bottleneck. Secondly, we show that the first-best pricing scheme with bottleneck permits for this V-shaped network does not always achieve a Pareto improvement, with the cost of one group of drivers is increased by the permit pricing, a phenomena akin to the bottleneck paradox of Arnott, de Palma, and Lindsey (1993). We propose three implementations of bottleneck permits for Pareto-improving: (1) merging priority rule is included in the bottleneck permits scheme by creating different market for each origin; (2) the permit revenues are refunded as monetary compensation to drivers whose cost is increased; and (3) the permit revenues are used to expand bottleneck capacity. For each implementation, we derive their equilibrium solutions and demonstrate that the Pareto improvement is achieved and social cost is decreased by using the permit revenues for expanding the bottleneck capacity.

Estimation of Passenger's Train Choice in Railway Network with Smart Card Ticket System

近年,鉄道などの公共交通機関でICカード乗車券(交 通系ICカード)の導入が進んでいる.交通系ICカードの 乗車履歴データ(以下ICデータと呼ぶ)は,各鉄道会社で 管理されており,鉄道利用者に対するマーケティング等 への活用が期待されている. ICデータの特徴には,1ICカード(利用者)毎の利用 履歴が記録されていること,2改札通過時刻を1分単位 という詳細な時間解像度で記録していること,3長期に わたる改札通過の観測データが収集できること,4入出 場記録が完結されなければ利用者は継続利用できないた めデータの欠損が少ないことがあげられる.これらの特 徴よりICデータは,時刻毎の利用者の鉄道利用の状況の 詳細な分析や,利用者行動の長期にわたる変動を分析す るのに適したデータであるといえるだろう. これまでの鉄道利用者の行動を推定する研究では, 大都市交通センサスによるデータを用いる方法が主流で あった.しかし,センサスを用いた方法では,都市 全体の鉄道旅客流動を把握することが可能である半面, 日々の変動をとらえることはできない.一方,各車両の 応荷重の履歴データや自動改札機の通過履歴によるデー タを用いることで日々の旅客の変動を推定することも可 能になってきている.これらの手法では,個々の車 両の乗客数や駅間を移動する乗客数の日々の変動を捉え ることが可能である.しかし,これらのデータを用いた 方法でも,各利用者を識別した上で変動を捉えることは できない.これに対し,ICデータでは,利用者毎の乗降 駅と乗降時刻を知ることができる.その情報から,利用 者毎に乗車列車を推定できれば,様々な利用者層での列 車選択の特性を明らかにすることが期待できる. 本研究の目的は,ICデータと列車ダイヤを用いて,