Search

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. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a data fusion methodology for estimating behavioural attributes of trips using smart card data to observe continuous long-term changes in the attributes of trips. The method is intended to enhance understanding of travellers’ behaviour during monitoring the smart card data. In order to supplement absent behavioural attributes in the smart card data, this study developed a data fusion methodology of smart card data with the person trip survey data with the naïve Bayes probabilistic model. A model for estimating the trip purpose is derived from the person trip survey data. By using the model, trip purposes are estimated as supplementary behavioural attributes of the trips observed in the smart card data. The validation analysis showed that the proposed method successfully estimated the trip purposes in 86.2% of the validation data. The empirical data mining analysis showed that the proposed methodology can be applied to find and interpret the behavioural features observed in the smart card data which had been difficult to obtain from each independent dataset. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new formulation for the dynamic stochastic traffic assignment problem. [•] Treat route traffic packets as random variables. [•] Acquire the probability distribution of the random variables. [•] Estimate the characteristics of the probability distribution. [ABSTRACT FROM AUTHOR]

Highlights: [•] This study proposes a statistical model to estimate route traffic flows in congested networks. [•] We formulate the problem as a conditional distribution of route flows. [•] The Bayesian theorem is applied to derive the conditional distribution. [•] A hierarchical form is designed to integrate prior knowledge. [•] Characteristics of route/link traffic flows are estimated using a blocked Metropolis–Hastings (M–H) algorithm. [ABSTRACT FROM AUTHOR]

SUMMARY In this study, we consider that inter-day traffic flow fluctuations in a network are caused by stochastic travel behavior. We treat route traffic flows at each time interval as random variables. Therefore, the solution of the stochastic assignment problem should be the conditional joint probability distribution of the route flows given that the network is in stochastic user equilibrium. We formulate the conditional joint distribution and develop a Gibbs sampler to draw samples from the conditional joint distribution. The characteristics of the route flows at each time interval during the time horizon can be estimated on the basis of the simulated samples. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Recently, tracking-type travel data collection methods using mobile communication instruments have developed rapidly. The detailed and accurate travel data are useful for understanding travel behaviour and evaluating the actual effects of transport policy such as traffic demand management. This paper studies a simple index of a tourist behaviour using tracking data collected with a mobile instrument. Based on the proposed index and actual data collected in Kobe, cluster analysis is applied to find the topological characteristics of tourist behaviour. [Copyright &y& Elsevier]

Data collection methods that use mobile phones have been developed for tracking, monitoring, and analyzing travel behaviour. Previous methods using a Personal Handy Phone System (PHS) assumed that the location data of base stations were known. This paper shows a positioning method for a mobile object when base station location data are unavailable. Instead of base stations, signal strength vectors (reference vectors) are observed at many places in a target area. Mobile objects also observe a signal strength vector (target vector). A similarity index of reference and target vectors is used to calculate the location of a mobile object. This method tracked the ambulatory behaviour of tourists in an urban setting. [ABSTRACT FROM AUTHOR]

This paper shows the fundamental concepts and methodologies of using mobile communication instruments for tracking survey for individual travel behaviour in urban space. In particular, the characteristics of the tracking method using cellular phone are discussed. In addition to tracking type data collection procedure, the data transfer and labelling algorithms are proposed for providing various trip attributes to each point included in the location positioning data. The accuracy of the location positioning is found satisfactory for describing metropolitan scale travel behaviour. Tracking a hundred of event spectators has proved that the survey system would be available for on-line monitoring of a number of travellers in urban area. It is concluded that the proposed methodologies could be added in the toolbox of travel measurement. [Copyright &y& Elsevier]

There are various activities now taking place in ITS research and development in Japan. Advanced information and communication technologies have been applied to improve public transport systems, as well as automated highway systems. In the first part of this paper, we show three examples of public transport systems recently developed in ITS environment. These transport systems are operated in local cities and towns in Japan: the travel information system for tram users in Hiroshima, the demand responsive bus system in Nakamura and the co-operative use of electric vehicle in Ebina. In the second part of the paper, we explain how we have monitored individual passenger on public transport using cellular phones for location positioning. Location positioning technology for mobile object is essential for the operation and management of ITS supported public transport systems. Furthermore, such accurate and detailed positioning data can be utilized for travel behaviour analysis in demand modeling. The mobile instrument and monitoring systems shown in this paper can be combined with any of the case studies of ITS application to public transport systems. [ABSTRACT FROM AUTHOR]

When we evaluate the performance reliability of a network, it is necessary to describe user's behaviour in a partially degraded network. This paper shows that the Stochastic User Equilibrium (SUE) model assuming user's route choice behaviour under uncertain network conditions can be incorporated in the performance reliability model. The effects of providing information are analyzed using the SUE model with two different groups of route choice; informed drivers and non informed drivers. It is found that providing information generally increases network performance reliability. This depends, however, on the probability distribution of the network states. [ABSTRACT FROM AUTHOR]

• We build a node-place-ridership model and compare three transit operators. • Node value has five times higher positive impacts than place value on ridership. • Stations balanced between node and place can be uncoordinated with ridership. • Japan Railway has better node-place-ridership coordination than other operators. • Stations unbalanced between node and place can coordinate with ridership. Balancing network accessibility (node value) and station-area land use (place value) has been an important research topic for transit-oriented development, but little was known about how node and place value should coordinate with ridership. Using Tokyo as a case study, we develop a node-place-ridership model to evaluate coordination between these three facets and compare different transit operators, namely subway in central area, private railway in suburban area, and Japan Railway (JR) in both central and suburban area. An indicator framework is established to measure the node value by accessible opportunities, network centrality, and train service, and measure the place value by density, design, and diversity. Using open-source urban data, we generate a series of node and place indicators for each station and develop a composite node value index and place value index with information entropy weighting. The results show that node value and place value have positive impacts on ridership and node value's impact is five times higher than place value. Stations balanced between node and place value can be uncoordinated with ridership, while stations unbalanced between node and place value can coordinate with ridership. JR has better node-place balance and ridership coordination than subway and private railway. Several implications are drawn for ridership coordination with node and place value, including planning for a more mixed urban structure and using through trains to connect central and suburban transit networks. The results of this study would inform transport planners of better organizing transit network, land use, and ridership in urban space. [ABSTRACT FROM AUTHOR]

This study focuses on the trajectory planning problem for automatic lane-change maneuvers, for which we propose a nonlinear programing model to generate the collision-avoidance lane-change trajectory in a spatiotemporal space. In this model, we regard a lane-change trajectory as the combination of a lane-change path and its velocity profiles. Based on the polynomial curve, the lane-change path is planned first in 2-dimensional Cartesian coordinates to connect the initial position with final position, and then a nonlinear mathematical programing model is used to generate the velocity profiles for maintaining the driving safety and comfort. To solve the nonlinear model efficiently, we design an inverse strategy to set the initial guess that can coordinate with the discrete scheme of the trajectory planning problem. Moreover, as the vehicle shifts from a lane-change maneuver to a car-following maneuver at the end of the lane-change process, the planned final acceleration is constrained from the car-following model. Finally, a series of numerical examples are provided, and the results indicate that the automated vehicle can drive along the planned lane-changing trajectory safely and efficiently. [ABSTRACT FROM AUTHOR]

Ridesharing has been attracting attention in Western countries in accordance with the rise of social and economic systems in which goods and services are shared between individuals. In accordance with the spread of ridesharing services, users' data, which concern how, when, where, and why they use these services, are being gradually collected around the world. However, there are only a few studies that deal with the empirical situation in Japan. In addition, ridesharing services in the country are not yet as popular as those in Western countries. Therefore, in this study, we aim to show the present situation of the ridesharing behavior in Japan. We conduct an empirical analysis by using actual long-distance peer-to-peer ridesharing data in the country. Firstly, we classify ridesharing drives into three classes by OD pairs: Inter-metropolitan, Low-density area, and Others. Next, we formulate a binomial probit model that explains the matching success for each drive class. The estimated model shows that the departure time and day, days to departure from registration date, page views, and driver's past experiences are important factors for successful matching. Moreover, the similarities and differences across the drive classes are discussed through the estimated model. The sensitivity of each drive attribute is explained, and ways of promoting this ridesharing service are suggested by using the estimated parameters. [ABSTRACT FROM AUTHOR]

Probe vehicles provide some of the most useful data for road traffic monitoring because they can acquire wide-ranging and spatiotemporally detailed information at a relatively low cost compared with traditional fixed-point observation. However, current GPS-equipped probe vehicles cannot directly provide us volume-related variables such as flow and density. In this paper, we propose a new probe vehicle-based estimation method for obtaining volume-related variables by assuming that a probe vehicle can measure the spacing to its leading one. This assumption can be realized by utilizing key technologies in advanced driver assistance systems that are expected to spread in the near future. We developed a method of estimating the flow, density, and speed from the probe vehicle data without exogenous assumptions on traffic flow characteristics, such as a fundamental diagram. In order to quantify the characteristics of the method, we performed a field experiment at a real-world urban expressway by employing prototypes of the probe vehicles with spacing measurement equipment. The result showed that the proposed method could accurately estimate the 5 min and hourly traffic volumes with probe vehicle penetration rate of 3.5% and 0.2%, respectively. [ABSTRACT FROM AUTHOR]

Travel-time variation frequently occurs in urban arterial road networks as a result of demand and supply variations as well as of external factors such as adverse weather and natural disasters. Study of travel-time variation is useful for measuring travel-time reliability studies. The objective of this study was to analyse the factors influencing travel-time variation among multiple sections of the Kobe route of the Hanshin Expressway in Japan. In this study, the Seemingly Unrelated Regression Equation (SURE) model was adopted to analyse the travel-time variation due to traffic volume, traffic accidents and rainfall as factors from the supply side, demand side and external factors of the transportation system respectively. This study identified that there was contemporaneous error correlation among multiple sections of the Kobe route. This error causes significant difference in estimated model parameters between the SURE model and Ordinary Least Square (OLS) model. Particularly, it has been observed that SURE model coefficients obtained for the rainfall parameter were 42% lower than OLS model coefficients. The results of this study emphasize that the SURE model is good for analysing the influence of various factors on travel-time variation among multiple sections of the Hanshin Expressway. [ABSTRACT FROM AUTHOR]

Abstract: This paper provides a novel practical method for analyzing an anti-seismic reinforcement (ASR) problem involving hundreds of transportation facilities on an urban road network subject to multiple earthquake risks. The relevant properties of the present method are: (i) it evaluates the performance of an ASR strategy, taking into account traffic congestion and travelers’ trip-making or route-choice behavior; (ii) it estimates the realistic damage patterns on the road network and their occurrence probabilities on the basis of recent advances in structural and earthquake engineering; (iii) it has clear, sensible logic and includes neither a black-box nor a “lottery” in the necessary procedures. We examine the computational efficiency and whether the present method is reasonable by applying it to a test scenario of the Kobe urban and suburban area. [Copyright &y& Elsevier]

Abstract: Travellers attempting to make a non-recurrent trip cannot rely on their own experiences and, consequently, must rely on information generated by others. In such situations, travellers’ choices are subject to information transmissions through a social network; if this does not work well, they fail to make efficient choices owing to lack of information. This study proposes a model that describes the dynamics of information generation and transmissions and conducts an analysis to investigate the conditions that allow travellers to make efficient choices. It is shown that suppressing rapid diffusions of information and encouraging intentional searching for information can help travellers to make efficient choices. [Copyright &y& Elsevier]

Pedestrian behavior during evacuation has been formulated using various arbitrary microscopic methods to investigate the performance of crowd dynamics while their custom rules result in low visual realism in simulation due to the complexity of intrinsic decision logic of human. Statistical analysis is an effective way to reveal the motion pattern and path planning behavior of pedestrians whose main idea is to approach the trajectory and social attributes data of pedestrians extracted from evacuation drills as much as possible. In this study, we present a data-driven based microscopic pedestrian-simulation model with continuous-space representation to explore the potential of integrating empirical analysis into crowd simulation to enhance the authenticity of decision making. This method extracts the pedestrian's decision mode and smoothly applies it in the crowd dynamics model. Instead of navigating agents by arbitrary regulations, the desired direction of pedestrians during the motion is arranged by machine learning (ML) algorithms. The path decision module trained with actual pedestrian data improves the compatibility of the model in the application of various spatial scenarios and no longer suffers from tedious parameter fine-tuning work. To completely describe the information precepted by pedestrians, a polygon segmentation module is developed to divide the visual field of pedestrians and identify the mutual visibility among them. This module filters out the information that can be perceived by pedestrians in real situations, thereby bridges the gap between statistical analysis and numerical simulation methods. We compare different ML approaches for route-choice behavior prediction and discuss the relative importance of its influencing variables under different scenarios. Inferring the perception of social interactions from disaggregate choice data, the scope of effectiveness of conformity behavior and crowd-aversion are also discussed. The simulation results are compared with experimental data, illustrating the model's capability to accurately reproduce the observed flow motion in various scenarios with moderate modification in physical environment initialization. [ABSTRACT FROM AUTHOR]

Variable message signs (VMS) are used to provide dynamic information and one current application is to show different speed limits under different conditions. As speed is an important contributor to road accidents and also affects driver speed behavior, the present study focuses on how effective traffic advisory information is when helping drivers to divert from potentially dangerous conditions. Graphical representation of an Expressway section made it easy to isolate the effects of speed etc. by drivers with information provided through VMS under adverse fog conditions. Understanding and reacting to the VMS system by drivers is essential for its success. If drivers do not react by changing speed behavior then the VMS system will fail and further implementation may cease. In this paper an Analysis of Variance model, which is appropriate to the proposed experimental conditions, is used to study how subjects (drivers) will perceive provided information and also to find the effect of VMS on driver speed behavior on the simulated Expressway section. [ABSTRACT FROM AUTHOR]

A pedestrian's visual field constantly changes as they move around a room containing structural components or large furniture, which can block the pedestrian from perceiving visual information. Changes in position simultaneously update optional route sets and the perception of how crowded these paths are. In this study, a microscopic pedestrian–simulation model with continuous-space representation is developed to investigate the route-choice behavior of pedestrians in a space where obstacles block the visual field. Instead of potential-based navigation, the desired direction of pedestrians during the motion is expressed by an oriented network and delivered to the navigation layer: an optimal reciprocal collision avoidance (ORCA) system, which models pedestrians' local maneuvers and movement decisions in detail. We quantify the characteristics of the model by comparing the model results with two groups of experiments conducted under conditions of good, limited, and zero visibility. The performance results with two different layouts are compared through numerical simulation, illustrating the model's capability to accurately represent the observed crowd dynamics in the facility. Results from the sensitivity analysis and simulation scenario can benefit the evacuation guidance arrangement and internal component design in large public spaces. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including short-tem prediction models to predict average spot speeds, driver's dynamic route choice behaviour, and driver's route choice rules.

Recent comprehensive reviews on traveler behavior in transportation networks have been given by Asakura (1999), Alder (2001), Chatterjee et al. (2002), Bell (2002), Arentze and Timmermans (2005). However, the expansion of transportation networks and the emergence of new technologies have generated an urgent need for advanced models and solution algorithms so as to provide better understanding of the traveler behavior on networks in response to various changes. This special issue on 'Behavior in Networks' is a collection of selected papers presented at the International Workshop on Behavior in Networks held on 22nd −23rd July, 2004. The workshop placed greater emphasis on discussions of how new models and advanced methods can be used for giving better insights on travel behaviors in transportation networks particularly in view of the recent advancement of technologies. The workshop brought together experts in a behavior side as well as a transportation network side worldwide to discuss both recent research and future directions in this important field. The selected papers cover the important aspects of behavior in networks to enhance the realism of modeling in transport. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlights • New methodology of travel context (i.e., trip purpose) inference for an activity-travel survey, implementable to smartphones. • It aims to reduce survey burden on respondents while keeping quality of data high. • No a priori information (e.g., geographic data, pre-calibrated classifier) is required. • It automatically infers context based on GPS trace and a traveler-specific classifier. • It sequentially updates the classifier by occasionally asking a question to a traveler during a survey. Abstract This article proposes a framework for an interactive activity-travel survey method, implementable on mobile devices such as smartphones. The proposed method was developed to reduce the burden (i.e., frequency of questions) on respondents in long-term behavioral surveys, without relying on external data sources. The method employs an online travel context estimation model and an online machine learning method as interactive processes. The estimation model is used for automatically estimating travel contexts during surveys, while the online machine learning method is used for dynamically updating the estimation model, based on answers from respondents. The proposed method was examined by simulations using data obtained from a past probe person survey. The results suggest that the frequency of inputs by respondents in surveys can be significantly reduced, while maintaining high accuracy of the obtained data. For example, the method successfully estimated certain types of trips (e.g., commuting) and the behaviors of certain respondents (e.g., those whose activity-travel pattern is recurrent) because of the learning process and reduced survey burden on them. Meanwhile, although the method could not always precisely estimate some other types of trips, it eventually obtained accurate results because of the interaction process. Therefore, the proposed method could be useful to reduce the burden on respondents in long-term surveys, while maintaining high data quality and capturing traveler heterogeneity. [ABSTRACT FROM AUTHOR]

Highlights • A detector-free method for fundamental diagram estimation. • Using probe vehicle data and value of the jam density. • Guaranteed exact identification under idealized conditions. • Estimation algorithm for actual traffic conditions. • Validation using real-world probe vehicle data. Abstract The fundamental diagram (FD), also known as the flow–density relation, is one of the most fundamental concepts in the traffic flow theory. Conventionally, FDs are estimated by using data collected by detectors. However, detectors' installation sites are generally limited due to their high cost, making practical implementation of traffic flow theoretical works difficult. On the other hand, probe vehicles can collect spatially continuous data from wide-ranging area, and thus they can be useful sensors for large-scale traffic management. In this study, a novel framework of FD estimation by using probe vehicle data is developed. It determines FD parameters based on trajectories of randomly sampled vehicles and a given jam density that is easily inferred by other data sources. A computational algorithm for estimating a triangular FD based on actual, potentially noisy traffic data obtained by multiple probe vehicles is developed. The algorithm was empirically validated by using real-world probe vehicle data on a highway. The results suggest that the algorithm accurately and robustly estimates the FD parameters. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Several car manufacturers have proposed an alternative mode of auto transport called the low-speed private travel mode (LSPTM). It is a small single-seat electric vehicle that can run at speeds of around 5–20 km/h. Further, it is regarded as a tool to improve the mobility of pedestrians and encourage the use of public transport. The aim of this study was to estimate how the mobility of pedestrians will be improved and how the environment will be impacted by the introduction of this vehicle. At the outset, a stated preference survey of the modal choices is conducted to ascertain the users' preference for the LSPTM to walking based on the actual trips of the respondents measured using the probe person survey system. To evaluate how traveler's activities will be expanded by the LSPTM, a joint logit model was proposed to predict the destination and mode choices of travelers travelling from a train station in the central area of Kobe to their destinations with parameters estimated by the stated preference survey of the modal choices. Based on the model, it was concluded that the size of the area where half the number of people visit increased by 2.6 times after introducing the LSPTM at a fare of 50 yen. This can encourage people to use trains and the LSPTM instead of cars, which can cause the total reduction of the emissions. [ABSTRACT FROM AUTHOR]

SUMMARY This paper describes a new approach to developing a traffic flow model of traffic network simulators for the estimation of CO2 emissions by considering various measures such as the use of vehicle control systems and transportation demand management (TDM) strategies. Thus far, we have developed the traffic network simulator NETSTREAM (NETwork Simulator for TRaffic Efficiency And Mobility), which has been able to reproduce traffic congestion in large-scale networks because of its unique traffic flow model. This paper proposes an enhancement of this model: acceleration is also considered because it has a significant effect on the calculation of CO2 emissions. The proposed model is then verified and validated. The results indicate that it can reproduce acceleration in a way that closely agrees with the actual data, while maintaining the Q-K and K-V relationships. Finally, this paper presents an example evaluation of an acceleration control system. We confirm that the proposed traffic flow model can evaluate environmental improvements in large-scale networks. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Travel time reliability is a key indicator of system performance and has become increasingly important in today's world as businesses as well as households require on-time transportation for their activities. In order to analyze travel time reliability, an enormous amount of traffic flow data is needed. Recently, the development of advanced traffic flow data collection systems has enabled us to handle that. This article aims to examine the fundamental characteristics of travel time reliability measures using the traffic flow data from the Hanshin Expressway network. Differences and similarities in characteristics (average travel time, 95th percentile travel time, standard deviation, coefficient of variation, buffer time, and buffer time index) are investigated on one radial route (Route 11: Ikeda Line). The result shows that buffer time and buffer time index profiles have a similar tendency as those of the standard deviation and coefficient of variation, respectively. Differences in characteristics among five radial routes in the network are also investigated in order to explore each route's characteristics of time-of-day variation of traffic flow. Additionally, the effect of traffic incidents on travel time reliability measures is analyzed on one radical route (Route 14: Matsubara Line). The results show that traffic incidents are the dominant factor for travel times in off-peak hours on the Hanshin Expressway network. [ABSTRACT FROM AUTHOR]

The article presents an introduction to papers on traffic engineering that appear in this periodical.