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In recent years, studies on network vulnerability have grown rapidly in the fields of transportation and complex networks. Even though these two fields seem to be closely related, the overall structure is still unclear. In this study, in order to add clarity comprehensively and objectively, a citation network consisting of the vulnerability studies in the two fields is analyzed. Publication records are collected from the Web of Science and a citation network with 1,181 nodes and 4,601 links is constructed. Then the giant weakly connected component with 705 nodes and 4,584 links is extracted and analyzed. By performing a community detection algorithm, seven communities representing different research domains are identified. Then the major research development in each research domain is revealed by main path analysis. Finally we quantitatively reveal the asymmetric citation patterns between transportation and complex networks, which implies that mutual understanding between two fields is currently lacking. Since these two fields deal with vulnerability of networked systems in common, more active interdisciplinary studies should have a great potential to advance both fields.

In accordance with the spread of ridesharing services, users’ data, how, when, where and for what purpose they use the service, are gradually collected in the world. However, there are few studies that deal with empirical situations in Japan. In addition, ridesharing services in Japan are not yet popular compared with that in Western countries thus far. Therefore, in this study, we aim to show the present situation of ridesharing behavior in Japan. We conduct the empirical analysis by using actual long-distance peer-to-peer ridesharing data in Japan. Firstly, we classify ridesharing drives into three classes by OD pairs. Next, we formulate the binomial probit model that explains matching success for each drive class. Estimated model shows that departure time and day, days to departure from registration date, page views and driver’s past experiences are important factors for successful match make.

In an era of highly developed information and communication technologies, social networks play an important role in all aspects of businesses, including transportation. The use of dynamic ridesharing systems, which are systems in which travelers can find partner(s) to share their upcoming rides in real time, is also expected to be affected by the information propagated by friends on social networks. In this study, we investigate how such information in both online and offline social networks affects travelers’ decisions pertaining to ridesharing over days. Because travelers can choose which information to obtain, we also investigate whether the information collected only from nearby friends (in physical space) affects their decisions. To do this, we formulate a day-to-day behavior-based model of a dynamic ridesharing system. Results obtained from the numerical experiments, which are based on the formulated model, show that with the information propagation on social networks, the more friends that a traveler has, the greater is the number of travelers who use dynamic ridesharing systems.

Recent natural and man-made disasters have revealed the inherent weakness in mutually dependent critical infrastructures. A potential risk in such complex systems is that an influence of failures in one system can propagate along dependency and multiple systems can be broken simultaneously. Hence it is important to understand risks hidden behind dependency. Under the background, we investigate the impact of partial capacity reduction on network vulnerability against cascading failure. This study demonstrates that the partial capacity reduction increases the vulnerability of the whole network and failures can spread out easily. We also show that how critical nodes whose removals bring about devastating damage on the whole network appear in a network. Our results indicate a possibility that nodes which are located not only inside but also outside of the area of the capacity reduction can turn into critical nodes.

The study addresses the needs of detection and description of abnormal traffic patterns in large transportation networks formed due to the presence of unexpected disruptions, such as natural or manmade disasters. In order to take into account complex spatiotemporal structure of traffic dynamics and preserve multi-mode correlations, tensor-based traffic data representation is put forward. Further, with the reasonable assumptions on normal or expected traffic dynamics to exhibit similar periodic structure, the problem of abnormal or unexpected traffic patterns detection is treated as a low-rank modeling problem. More precisely, tensor robust principal component analysis is applied for the purpose of discovering distinctive normal and abnormal traffic patterns. For the validation purposes, continuum modeling approach is employed to emulate traffic dynamics, with consideration of the effect of aforementioned disruptions. The results suggested the applicability of proposed approach in order to extract abnormal traffic patterns in large transportation networks.

"Sharing economy", including car-sharing and ride-sharing, will change our society and transport in the near future. To effectively introduce ride-sharing service into our society, we need to determine route and schedule of vehicles based on users’ activity. We also need to understand how users’ activity patterns affect the performance of this sharing service, and we have to evaluate social benefit of introducing ride-sharing service. The objective of this study is to investigate how the ride-sharing service would be successfully operated in the above context. The optimal vehicle routing and scheduling problem which satisfies all the users’ activities is formulated as an integer linear programming problem. By using the proposed model, numerical experiments are conducted with different types of activity patterns. Then, efficiency of the optimized ride-sharing service is examined by comparing its performance with those of car-sharing service and fully private-owned car transport. The results show spatial and temporal distribution of users’ activity patterns will have great effects on the performance of ride-sharing services.

Automated and connected vehicles (ACVs) have received a great deal of attention. Indeed, their full market penetration will be desirable in terms of traffic efficiency, as ACVs can efficiently drive by precisely and instantaneously communicating, recognizing, and reacting to other ACVs. However, it is not yet certain whether traffic efficiency is improved in mixed traffic where ratio of manual vehicles is substantially high. This is because, for example, ACVs in mixed traffic may require excessive safety clearance, as they have to rely on relatively imperfect vision/radar-based vehicle recognition. Meanwhile, relative benefit of ACVs compared to manual vehicles would be proportional to travel time (because the most significant merit of ACVs for their driver is comfortable in-vehicle experience) and therefore severity of congestion. Consequently, equilibrium states of a myopic car market may suffer severer congestion and higher social cost than the current state—this is congestion paradox. This kind of phenomena can be considered as a consequence of market penetration of a good with network externality or social interaction, where market penetration of ACVs is endogenously determined based on their cost/benefit which depend on current number of ACVs users. This study analyzes this problem under idealized conditions. Specifically, a theoretical model of endogenous market penetration of ACVs considering changes in value of time, travel time, and transportation fare, which are the most direct impacts of ACVs to the society, is formulated. Then, its market dynamics is analyzed. Finally, strategic policies to avoid congestion paradox and achieve social optimum are proposed.

In this paper the possibility of utilization of wireless sensor network in application to human sensing problem is examined. With the emphasis on crowd dynamics monitoring, the most critical issues regarding this application are analyzed. Based on limitations and shortcomings of contemporary crowd monitoring approaches, as well as the specificity of the problem, modified positioning algorithm, combined with noisy data preprocessing technique is proposed. Moreover, physical principles of interaction between pedestrians and sensor network are analyzed. An experiment had been conducted to demonstrate the concept of data collection system aimed to obtain relevant information regarding pedestrian movements.

Smart ridesharing system (SRS) is a ridesharing system integrated with information and communication technology. We formulate a passenger matching problem by considering user preference in SRS. This study aims to investigate day-to-day dynamics and characteristics of passenger matching problem in SRS. A modified stable roommates problem is adopted to represent this matching problem. The characteristics of day-to-day dynamics (e.g., existence, uniqueness, and stability of equilibria) of SRS are understood and revealed by a simulation of formulated model. Through the simulations, the existence of multiple day-to-day equilibria is investigated under some conditions. One of the existing equilibria is an undesirable equilibrium where SRS is not feasible to be operated as no one rideshares, while the existence of a desirable stable equilibrium, where SRS is feasible to be operated, is realized under some conditions.

A fundamental diagram (FD), also known as flow–density relation, is one of the most important principles in traffic flow theory. It would be valuable if an FD could be calibrated by using GPS-equipped probe vehicles; since they can continuously collect data from wide spatiotemporal area, compared to traditional fixed sensors. This paper proposes methods for calibrating an FD from trajectories of sampled vehicles. We formulate a method that identifies values of a free-flow speed and a critical density of a tri-angular FD, while it relies on exogenous assumptions on FD's functional form and a value of its jam density. Then, a heuristic algorithm for FD calibration in actual traffic environment is developed based on the proposed method. It was validated using traffic data generated by microscopic traffic simulator. The results suggested that the proposed methods can calibrate an FD precisely and robustly. It implies that FDs in road sections on which congestion happens frequently can be calibrated using probe vehicles, if probe vehicle data were collected for a long period. Therefore, the proposed methods would contribute to significant improvement of applicability of probe vehicle-based traffic management methods.

Mobile communication instruments have made detecting traffic incidents possible by using floating traffic data. This paper studies the properties of traffic flow dynamics during incidents and proposes incident detection methods using floating data collected by probe vehicles equipped with on-board global positioning system (GPS) equipment. The proposed algorithms predict the time and location of traffic congestion caused by an incident. The detection rate and false rate of the models are examined using a traffic flow simulator, and the performance measures of the proposed methods are compared with those of previous methods.

This study proposes a Pareto improving congestion pricing using tradable bottleneck permits (TBP) scheme under the condition that marginal utility of toll cost changes among drivers. The TBP scheme is originally one of the first-best time-varying pricing schemes. However, it is not revealed that the scheme could achieve a Pareto improvement when marginal utility of toll cost changes among drivers. This study aims to analyze the effects of the TBP scheme on departure time choice of drivers and to propose a second best pricing scheme where a Pareto improvement can be achieved. In particular, we focus on a one-to-one network with a single bottleneck and employ a departure time choice model to discuss the case that there exists heterogeneity in schedule flexibility and toll resistance. Two classes for two attributes are assumed as the heterogeneity respectively: “busy/free” and “rich/poor”. Under the condition, we formulate the drivers’ utility changes caused by implementation of the TBP scheme. The cases with and without the TBP scheme are analytically compared. It is shown that the TBP scheme is not Pareto improving, where the utility of “busy-poor” drivers is decreased. In order to achieve a Pareto improvement, we propose partial implementation of the TBP scheme. In the partial implementation scheme, the bottleneck capacity is assigned to drivers with and without bottleneck permits, where the driver who has a bottleneck permit can pass through the bottleneck without congestion and a driver without it goes through congestion. As a result, we reveal conditions which can satisfy a Pareto improvement. This study finally discuss the requirements for a Pareto improvement that are derived by the proportion of the amount of issued bottleneck permits, the number of each class drivers, and the magnitude of heterogeneity.

This study proposes a new scheme for assigning traffic flows that aims to capture the stochastic nature of route traffic flows. We consider the route traffic flows to be random variables. The distribution of these random variables is formulated as a conditional probability distribution for a given assumption: the traffic network is in stochastic user equilibrium. From a Bayesian perspective, we treat the conditional distribution as a posterior distribution of route traffic flows, which is obtained using Bayes’ theorem. We develop a basic Metropolis–Hastings (M–H) sampling scheme, as well as a M–H within Gibbs sampling scheme, to draw samples from the posterior distribution. We estimate characteristics such as the means and variances of route traffic flows from simulated samples. The proposed model can directly output the route traffic flows, and has a highly flexible computation process.

This study proposes a statistical model to estimate route traffic flows in congested networks. In the study, it is assumed that route traffic flows conform to the stochastic user equilibrium (SUE) principle while being treated as random variables in order to exploit the stochastic nature of traffic. The proposed model formulates the distribution of these random variables as the conditional distribution of route flows and origin–destination (O-D) travel demand, given the observed link flows and the SUE principle. Here, the SUE principle is accounted for through the likelihood of user behaviours rather than by using a bi-level formulation. In this study, the Bayesian theorem is applied to derive the probability density function (PDF) of the conditional distribution. Based on the PDF, characteristics such as the means and variances of route/link traffic flows are estimated using a blocked Metropolis–Hastings (M–H) algorithm. To facilitate the use of prior knowledge, a hierarchical form is designed to provide a straightforward way to integrate prior knowledge into the traffic estimation model. The performance of the proposed method is tested on the Sioux–Falls network through a series of numerical examples.

Traffic incidents such as traffic accidents are the major causes of severe congestion in urban expressway networks. Providing traffic incident information to travellers is considered to be important for maintaining the level of service in urban expressways. The purpose of this study is to describe drivers’ route choice behaviours when traffic incident information is provided on a Variable Message Sign (VMS) in urban expressway networks. A stated-preference (SP) survey was conducted to observe travellers’ behaviours. The results showed that travellers can assume travel time of their alternative routes according to the congestion information of the road section provided by VMS.

Abstract: 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.

Tracking type data collection methods using mobile phone have been developed for monitoring and analyzing individual travel behavior. Previous methods using PHS (Personal Handy phone System) assumed that the location data of base stations were known. This paper shows a location positioning method of a mobile object when the location data of base stations are not available. Instead of base stations, signal strength vectors (reference vectors) are observed at many places in a target area. A mobile object also observes a signal strength vector (target vector). The similarity index of a reference vector and a target vector is used for calculating the location position of a mobile object. This method is applied to tracking travel behavior of tourists in Kobe Kitano area.