Fumitaka Kurauchi

Capacity Constrained Transit Assignment with Common Lines

This paper proposes the use of absorbing Markov chains to solve the capacity constrained transit network loading problem taking common lines into account. The approach handles congested transit networks, where some passengers will not be able to board because of the absence of sufficient space. The model also handles the common lines problem, where choice of route depends on frequency of arrivals. The mathematical formulation of the problem is presented together with a numerical example.

Using Bus Probe Data for Analysis of Travel Time Variability

The rapid progress of information technology (IT) may provide us with new insights into understanding traffic phenomena, and could help mitigate traffic problems. One of the key applications of IT to traffic and transport analysis is the identification of the location of moving objects using the Global Positioning System (GPS). It is expected that detailed traffic analysis could be carried out using these data. In this article, we first summarize the various applications of probe data in transport analysis. GPS data are merely a sequence of locations, and further data transformation such as map-matching, data-reduction, processing, and reporting is needed to use them effectively. We then discuss the application of bus probe data to evaluating travel time variability and the level of service (LOS) of roads. A methodology for evaluating the road network from the viewpoint of travel time stability and reliability using bus probe data is proposed. Travel time distributions of arbitrary routes are estimated by statistically summing up directly observed multiple travel time distributions. Based on the development of methodologies to estimate travel time distributions of arbitrary routes covered by the bus probe survey, this study proposes an approach to evaluate the LOS of road networks based on the concept of travel time reliability.

Network Evaluation Based on Connectivity Vulnerability

Network reliability indices are generally expressed as a multiplier of the probability that the specific event may occur and the consequence of the event. It means that an inaccurate estimation of the probability of event occurrence may lead to different evaluation of the reliability. In contrast, the concept of “network vulnerability” has been proposed for evaluating the network component only by the consequence of the degradation. Though the concept of vulnerability may have avoided the uncertainty of the capacity degradation, it still requires an exact measurement of the traffic demand in the network which may not be accurate especially in the case of the disaster. We thus propose the method of critical link identification from the topological point of view, i.e., connectivity vulnerability. The concept of the k-edge-connectivity is applied in this study. The number of distinct paths with acceptable travel time between each origin-destination (OD) pair is used to measure the connectivity of that OD pair (similar to the concept of k-edge connectivity). A mathematical program for identifying acceptable distinct paths between each OD pair is formulated. The proposed method and indicator of connectivity vulnerability is then tested with the Kansai road network.

A Game Theoretic Approach to the Determination of Hyperpaths in Transportation Networks

In transit assignment, the common lines problem leads to the notion of a hyperpath, which is a set of paths that when used according to the “take whichever attractive line arrives next” strategy minimises the expected travel time. Similarly, the game theoretic approach to risk-averse traffic assignment leads to the generation of a set of paths which minimises expected travel time when a pessimistic assumption is made about on-trip events. The equivalence between the hyperpath of transit assignment and the set of paths generated by a multi-agent, zero sum game is shown in this paper. In particular, game theory is used to show that the path split probabilities proposed by Spiess and Florian (1989) are optimal for the risk-averse traveller who needs to make an on-the-spot decision between alternative routes. An alternative two-agent (single demon), zero-sum game is considered. The results of the multiple- and two-agent games are compared on a small example network, showing that the single demon game can lead to denser hyperpaths.

Traffic management system against major earthquakes

This paper describes fundamental issues of the traffic management systems and the actual traffic conditions following the Hanshin-Awaji Earthquake. Based on these issues, a new concept of traffic management system against an earthquake disaster is proposed, and the effectiveness of 2-stage area traffic regulation is examined through the case study for accommodation of personal passenger car demand immediately after the earthquake. Additionally an integrated traffic management system against a major earthquake and the related issues are discussed. It is expected that ITS technology will play an important role in the future development of traffic management system during an earthquake disaster.

Using Electronic Toll Collection Data to Understand Traffic Demand

In this study, we explored the potential of using electronic toll collection (ETC)-derived data that are a part of intelligent transport systems (ITS). Dynamic origin–destination (OD) traffic volumes were estimated using ETC data on the Hanshin Expressway. A dynamic OD estimation model that was suggested in a previous study was used, and abundant ETC data were input to improve the estimation accuracy. The results of OD estimation were analyzed to understand traffic demand and its variation. External factors were clarified that have an influence on variances in the OD flows, and statistical analysis methods for the variations were proposed depending on the factors. Moreover, the improvements in traffic simulation accuracy and performance as a result of using ETC data as input variables in the simulation models were discussed. According to the results of this study, ETC data have potential to assist in understaningd traffic demand and its variation, and the results can be applied to network management.

Analysis of variation in demand and performance of urban expressways using dynamic path flow estimation

Understanding the dynamic characteristics of traffic demand is a key issue in designing a more reliable transport network. Identifying variations in link flow is easy, but it is impossible to determine variations in demand directly from observed traffic data. In this study, demand fluctuation was analysed using the dynamic path flow estimation method. Path flows were estimated using 1 year data from the Hanshin Expressway in Japan, and demand variations were analysed using the estimated results. This article also discusses the relationship between demand variations and external conditions such as weather and commercial practises. The results provide a valuable background for more detailed traffic control measures.

EVALUATION OF ROAD NETWORK RELIABILITY CONSIDERING TRAFFIC REGULATION AFTER A DISASTER

Abstract Road network performance when a large disaster happens depends on how the road traffic is regulated. To evaluate the performance of the road network, one should consider the traffic regulation in an emergent condition. Authors have proposed the idea of area traffic regulation when a large disaster occurs (Iida, et al., 2000). In the former paper, a bi-level optimisation model to calculate the optimal regulation ratio is proposed. This paper proposes an efficient and fast method to calculate regulation ratios for two-stage road traffic regulation. The simplified model adopts linear programming method instead of bi-level optimisation method, which is quicker and simpler. The simpler algorithm contributes to conduct various kinds of case studies, and helps finding a reliability of the road network considering traffic regulations after a disaster.

Public Transport Planning with Smart Card Data

Smart card transactions represent a passively collected source of information on passenger travel. With geographic coordinates and time stamps for these transactions, it is possible to infer the passenger’s origin and destination of a journey. In cases where only one transaction takes place at the origin stop during a journey or trip leg (a so-called “tapon”), an alighting location must be inferred. This chapter reviews the common methods and assumptions guiding inference of destinations. To supplement this review, it considers methods that convert the origins and destinations from smart card transactions into estimates of origindestination flows (O-D matrices). Such estimates may be complicated by the interpretation of the smart card data, particularly with respect to activities that might occur at transfer locations. Finally, this chapter explores other methods employed to look at patterns in O-D journeys and in passenger tours throughout a day. Several avenues for continuing research in these areas are highlighted.

Special Section on Dynamic Traffic Assignment: A Tool for Evaluating and Assessing Dynamic Traffic Management Schemes

Intelligent transportation system (ITS) technologies have enabled efficient traffic control. One important issue pertains to extensions of traffic management and data collection schemes over the temporal dimension. For instance, dynamic traffic information extracted from electronic data can improve the spatial concentration of traffic flow, and provide more detailed and accurate origin–destination (OD) information. To discuss the effect of such traffic management measures, we need a model to forecast the time-varying traffic conditions as accurately as possible. Dynamic traffic assignment (DTA) is indispensable for evaluating traffic operations and for planning. In this special issue, we have four articles discussing the use of DTA for implementing and evaluating traffic management schemes, based on ITS technologies. The first article, by Chen et al. (2014), reports on an investigation of the time-dependent reliable shortest path problem, to help travelers choose their optimal route under travel time uncertainty. They considered two aspects of the shortest path problem: first, the determination of the earliest arrival time and associated reliable shortest path for a given departure time, and second, the determination of the latest departure time and associated reliable shortest path for a given preferred arrival time. They approached the optimal traffic route determination as a mathematical problem, providing key insights into proper characterization of this problem, as well as efficient algorithms for