Provision Of Traffic Information Research Articles

Critical examination of multilane free-flow tolling system implementation in Indonesia

After operationalization, Indonesia’s multilane free flow (MLFF) tolling system will meet new challenges. Many think that this tolling method will be beneficial not only for the toll road user but also for toll road operators (TROs) and transportation authorities. On the contrary, the drawbacks are also being considered in several reports and discussions. Moreover, too little research to shed light makes the complexity of the MLFF tolling system remain largely uncharted. This paper critically examines the Indonesian MLFF tolling system, questioning prevailing assumptions and encouraging further study. Employed literature review of all related documents that mentioned electronic toll collection, specifically on MLFF and open road tolling (ORT) system, this study offers valuable insights into the practical effects in real-world situations. The toll road authority should re-evaluate some regulations concerning minimum service standards imposed on the TROs. The privacy issues as an excess of using Global Navigation Satellite System (GNSS) that tracks each toll road user appears to become mainstream, while others deserve attention. The traffic information provision as a tool of traffic management should be considered as one of the solutions to provide valid traffic information derived from a scientifically sound method based on verifiable probe data.

Study of traffic safety on the street and road network of Ternopil

The road safety issues in the city of Ternopil are studied in this article. The found actual deficiencies were structured according to the main indicators. Based on the analysis of the movement of traffic flows in the morning period, the most problematic sections of the SRN (street and road network) were identified. In general, the speed of traffic flows along the SRN in the evening peak period was found significantly lower than in the morning peak period. To analyze road traffic safety in the city of Ternopil, a cartographic method was applied. Based the obtained results, the road accidents in Ternopil mostly occur along main streets without clearly defined places of concentration. In this regard, a conditional indicator of the danger of transport corridors of Ternopil was derived, which allows analyzing the predominant location (clustering) of road accidents with victims in each district of the city. The current policy and practices of Ternopil in the field of road safety management need improvement. However, the volume of performed measures does not fully cover the need in traffic safety insurance and information provision of traffic participants. In general, it can be stated that proactive actions in the field of ensuring traffic safety are just starting in Ternopil (“starting city”). Thus, it differs favorably from the vast majority of other regional centers, in which the problem of traffic safety has not been even discussed so far. In addition, the plan for sustainable urban mobility is under consideration in the Ternopil City Territorial Community.

A Case Study on the Route Change Effects of VMS-Based Traffic Information Provision

A Case Study on the Route Change Effects of VMS-Based Traffic Information Provision

Online Experimental Study on Traffic Information Provision Based on the Self-fulfilling Signal

Online Experimental Study on Traffic Information Provision Based on the Self-fulfilling Signal

The Impact of Traffic Information Provision and Prevailing Policy on the Route Choice Behavior of Motorcycles Based on the Stated Preference Experiment: A Preliminary Study

It is anticipated that the prevalence of motorcycles in Asian countries will continue to increase, causing congestion and network imbalances concerning the nature of motorcycles. Literature demonstrates Variable Message Signs (VMSs) as an effective measure for addressing this issue. Understanding route choice behavior may thus aid in determining the appropriate traffic information to broadcast. This study aims to identify the impact of VMS messages related to traffic conditions and regulations on the route choice of motorcycle riders. In this instance, the core concept of ramp metering is adapted for non-highways to manage the proportion of motorcycles entering the traffic stream of the mainline. Two predetermined routes were offered through a stated preference survey to capture the responses to VMS. A binary logit model was initially introduced, further improved by including the individual characteristics and accommodating the unobserved factors across a series of observations (panel effects) by applying the mixed binary logit. It was revealed that traffic flow conditions significantly affect route preference; therefore, motorcycles tend to choose routes with lower volumes. However, waiting time at a ramp meter has no impact. The present research is a preliminary investigation for further implications in proposing traffic management strategies under mixed traffic situations.

Deep Multimodal Learning for Traffic Speed Estimation Combining Dedicated Short-Range Communication and Vehicle Detection System Data

The estimation of traffic variables and provision of traffic information are the most important components of intelligent transportation systems. Advances in technology have led to the collection of various traffic sensor data, and nonlinear dependencies between traffic variables have enabled the development of models based on deep learning approaches. However, there is a missing data segment where data collection is not possible because of the non-installation of the sensor, malfunction of the sensor, or error in communication. In this study, a deep multimodal model is proposed for traffic speed estimation of the missing data segment. We implement the proposed model using two heterogeneous traffic sensors, that is, a vehicle detection system and dedicated short-range communication. The structure of the proposed model consists of three multilayer perceptron models, two of which receive each modality as input data and one fusion model that receives the concatenated outputs from each modality model as input data. To evaluate the estimation performance of the deep multimodal model, we use three performance measures to compare the multimodal model with the arithmetic average model and a single-modality model. The results show that the single-modality model and the proposed deep multimodal model outperform the arithmetic average model. In particular, the deep multimodal model shows the highest accuracies of 90.5% and 92.1% on weekends and peak hours, respectively, without reflecting the true value. The proposed deep multimodal model has three contributions, that is, high accuracy using two different sensors, robustness in various periods, and real-time application with fast computational time.

Formation of multiple aerial LED signs in multiple lanes formed with AIRR by use of two beam splitters

We propose multiple aerial imaging system by use of two beam splitters in the optical system of aerial imaging by retro-reflection (AIRR). The AIRR optical system consists of a light source, a beam splitter, and a retro-reflector. Use of two-layered beam splitters enables us to show multiple aerial signs to drivers in multiple driving lanes. The purpose of this paper is to confirm feasibility that an aerial display by use of AIRR for a novel traffic information provision. First, we explain an optical configuration to show aerial images to multiple road lanes, a driving lane and an overtaking lane with AIRR by use of two beam splitters. Next, we have developed a prototype optical system by use of two large beam splitters and a large high-brightness LED sign that is designed to be used for actual road displays. The light source was an LED display with a height of 450 mm and a width of 390 mm, which shows a character. We observed aerial images by naked eyes as experiments. And we discuss the issues for practical application that the experiments have revealed.

Heterogeneous traffic information provision on road networks with competitive or cooperative information providers

This paper studies the long-term effects of heterogeneous information provision on road networks with multiple information providers (IPs). To capture the information heterogeneity resulting from various IPs, we consider that users subscribing to different IPs receive non-identical information sets about the available routes and make route choices within their information sets. We investigate both IPs’ strategic interactions and users’ route choices in a bi-level “leader–follower” game. At the upper level, IPs act as leaders who compete or cooperate to determine the optimal information sets provided for their users to maximize their objectives. Three models are developed to investigate IPs’ strategic interactions, i.e., Nash game, Stackelberg game, and cooperation, in which IPs are non-cooperative, leader and followers, and cooperative, respectively. At the lower level, users act as followers to choose their user-optimal routes based on their received information sets. Wardrop Equilibrium with Multiple Information Classes (WEMIC) is defined to capture users’ route choices. The WEMIC problem is formulated as an equivalent variational inequality (VI) problem. The existence and uniqueness of the VI solution are proved. When IPs play the Nash game, the problem is formulated as an Equilibrium Problem with Equilibrium Constraints (EPEC) with discrete variables and non-continuous payoff functions. To solve the EPEC efficiently, we propose a Binary Coded Nash Dominance based Evolutionary (BCNDE) algorithm embedded with a heuristic method for action space reduction. Finally, numerical experiments with two IPs (a big IP and a small IP) are conducted to analyze the effects of unit information cost in the Nash game and the impact of the leader’s strategy in the Stackelberg game, respectively. We find that the small IP may benefit from higher information costs in the Nash game. Moreover, the effects of IPs’ competition and cooperation are compared from both the perspectives of IPs and the system. The results indicate that cooperation can reduce congestion and make both IPs better off. On the Nguyen–Dupuis network, we demonstrate the effectiveness of the BCNDE algorithm and analyze the effects of market share distribution in the Nash game. We also extend this study by considering elastic demand. The extended models, theoretical proofs, and corresponding numerical experiments are provided.

Special Issue on Future Intelligent Transportation System (ITS) for Tomorrow and Beyond

Intelligent Transportation System (ITS) has evolved into a system for provision of traffic information and traffic control with the help of advanced IT technologies [...]

EUROPEAN ITS PLATFORM: RETHINKING ITS

The expectations placed on the existing European transport infrastructure are increasing due to the growing mobility needs of the population on the one hand and the rising volume of freight traffic on the other. The aim of the European ITS Platform is to increase the efficiency of the TEN-T core network through a better use of the existing infrastructure by implementing Intelligent Transport Systems (ITS). It fosters interoperability and the development of uniform technical standards by monitoring and disseminating the results delivered by the five CEF ITS Road Corridor Projects.One major outcome is the drafting of the “Reference Handbook for harmonized ITS Core Service Deployment in Europe”. This digital freely available handbook comprises a series of guidance and advice for use by road authorities and operators to support them in the development of their strategic approach, design development, deployment, installation and operation of ITS and remain compliant with European legislation. Its key features include: Deployment Guidance for 14 different ITS Services, including DATEX reference profiles, Guidance on the provision and use of traffic information provided under the European Directive (2010/40/EU), C-ITS developments, Knowledge gained from the collection of 100 Best Practices etc.

엣지카메라기반 실시간 도로위험 검지 및 정보 제공 시스템

현재 C-ITS 서비스는 레이다 혹 카메라 기반으로 돌발상황을 검지하고 도로위험 정보를 운전자에게 제공한다. 레이다기반의 돌발상황검지기는 가격이 비싸고 교통 이상 상황을 확인하기 위해서는 별도의 카메라가 필요하다. 또한, 레이다는 차량과 보행자 검출이 가능하나 그 외 오토바이, 자전거, 낙하물과 같은 객체는 검출이 어렵다. 기존 C-ITS 시스템에서 카메라기반의 돌방상황검지의 경우, C-ITS 센터의 영상분석 서버에서 돌발상황을 검지하고 위험정보를 운전자에게 제공하기 때문에 실시간 서비스를 제공하기 어렵다. 이러한 문제를 해결하기 위해 본 논문은 엣지카메라기반 실시간 도로위험인식 및 정보제공시스템을 제안한다. 엣지카메라는 로컬에서 영상처리를 통해 객체를 인식하고 위험상황을 판단하여 RSU를 통해 운전자에게 위험정보를 제공한다. 제안 시스템은 실시간 처리를 위해 경량 딥러닝 네트워크를 사용하고, 정확도 향상을 위해 지역최적화를 수행한다. 그리고 기존 C-ITS 응용 표준 프로토콜을 수정 없이 사용할 수 있도록 엣지카메라와 RSU의 통신 인터페이스를 설계하였다. 실험을 통해 교통객체를 정확하게 인식하고 100ms 이내에 실시간으로 도로위험상황을 운전자에게 정보를 제공함을 확인할 수 있다.

Cloud-Assisted Mobile Crowd Sensing for Route and Congestion Monitoring

Accurate and reliable real-time urban traffic management can benefit urban citizens’ daily life by reducing stress, travel time and carbon footprint. The provision of reliable and accurate traffic information has however proven to be a major challenge in intelligent transportation systems. Citizens carrying smartphones can be exploited as an important provider of traffic information and the mobile crowd sensing paradigm can be used as a solution to this challenge. In this paper, an urban traffic monitoring system which exploits the power of participatory sensing and cloud messaging is proposed. Crowd intelligence that is used to estimate traffic congestion levels, arrival times, and average road speed is harvested from crowd sensed data. Traffic congestion control at route level is implemented with a route guidance system. Proactive warnings or recommendations to drivers in the vicinity of or on the route to reported events are provided. The drivers can also report short-term traffic events and physical road conditions for road monitoring. Real-world experiments are conducted with a prototype implementation and the results demonstrates both system feasibility and traffic estimation accuracy.

Heterogeneous Traffic Information Provision on Road Networks with Competitive or Cooperative Information Providers

Heterogeneous Traffic Information Provision on Road Networks with Competitive or Cooperative Information Providers

Demand-Oriented Approach to Estimate the Operational Performance of Urban Networks With and Without Traffic Information Provision

This paper addresses the problem of estimating the operational performance of extended urban transport networks under different conditions of traffic information availability. The suggested approach employs a simulation procedure for the real-time estimation of the network state, based on the dynamic assignment of an O-D matrix, which is adjusted by the prevailing traffic conditions. Next, a different procedure is used for the online simulation of the route diversion behavior of users under the influence of route guidance information. Moreover, the approach provides the prediction of the network state by hypothesizing about the adjustment of the travel behavior of users to the new traffic conditions generated by the routing information. The implementation of the approach on a part of a real urban network led to the determination of suitable information updating frequencies for providing the largest improvements in the various performance measures. The results signify the important role of taking into account dynamic changes in the O-D demand size for the plausible evaluation of the impact of information provision on the network performance for real-time traffic operations and transport planning purposes.

Active traffic management in urban areas: Is it effective for professional drivers? The case of variable message signs

Active traffic management is the ability to dynamically manage congestion based on prevailing and predicted traffic conditions. Several technologies and algorithms have been employed to develop a set of tools utilised by Traffic Control/Traffic Management Centres with the aim to mitigate disruptions and improve traffic flow in the road networks. One such tool, belonging to the Advanced Traveller Information Systems category is Variable Message Signs. Variable Message Signs are located in major roads and provide real-time traffic information to the drivers so as to modify their behaviour considering the prevailing traffic conditions. To this end, the question arises. Is active traffic management in urban road networks – as currently provided by Variable Message Signs – effective for professional drivers? In plain words, do professional drivers modify their route choice, i.e. divert under the provision of real-time traffic information? A stated preference survey was performed to truck and taxi drivers to identify their response to information provided by Variable Message Signs. Results, in the form of ordered probit models indicated that the provided information is not that relevant to the investigated driver categories, as it is for passenger car drivers.

A comparative study of centralised and decentralised architectures for network traffic control

ABSTRACTThis paper presents a comparative study of centralised and decentralised architectures for managing urban road network efficiency with consideration of users’ responses and uncertainties with respect to prevailing traffic conditions. The control systems are applied to different network topologies with different levels and spatial distributions of traffic demand. The study reveals that the computationally effective decentralised systems could perform almost as well as the conventional centralised one when users’ responses are taken into account with provision of real-time traffic information. This generates new insight on cooperative transport management with applications of information and communication technology.

Real-Time Traffic Information Provision Using Individual Probe and Five-Minute Aggregated Data

Real-Time Traffic Information Provision Using Individual Probe and Five-Minute Aggregated Data

Integrated framework for real‐time urban network travel time prediction on sparse probe data

The study presents the methodology and system architecture of an integrated urban road network travel time prediction framework based on low-frequency probe vehicle data. Intended applications include real-time network traffic management, vehicle routing and information provision. The framework integrates methods for receiving a stream of probe vehicle data, map matching and path inference, link travel time estimation, calibration of prediction model parameters and network travel time prediction in real time. The system design satisfies three crucial aspects: computational efficiency of prediction, internal consistency between components and robustness against noisy and missing data. Prediction is based on a multivariate hybrid method of probabilistic principal component analysis, which captures global correlation patterns between links and time intervals, and local smoothing, which considers local correlations among neighbouring links. Computational experiments for the road network of Stockholm, Sweden and probe data from taxis show that the system provides high accuracy for both peak and off-peak traffic conditions. The computational efficiency of the framework makes it capable of real-time prediction for large-scale networks. For links with large speed variations between days, prediction significantly outperforms the historical mean. Furthermore, prediction is reliable also for links with high proportions of missing data.

Simulation study of the impact of local real-time traffic information provision strategy in connected vehicle systems

Abstract Current vehicle navigation systems usually rely on global real-time traffic information. They suggest the best paths to travelers through an independent and selfish-routing mechanism. Existing studies show that such systems have a great potential to cause traffic congestions oscillating among several alternative corridors, i.e., iteratively leading exceeding traffic into light traffic corridors and making congestion on one and then another within a short time period. To address this issue, this research proposes a local information provision strategy, given travelers are still guided by an independent and selfish-routing mechanism. The coverage of the real-time information provision is identified by the minimum bounding rectangle, and the ellipse method respectively. The benefits of the proposed strategy in system level are investigated by the simulation studies built upon the Borman expressway network. The simulation results indicate that as the local travel time information provision strategy is used, it may save 10–25% of the system travel time resulting from global information provision strategy under medium or high network traffic loads. Moreover, the sensitivity analyses indicate that the merits of the local travel time information provision strategy are more significant as the penetration of smart vehicles increases over the network. Thus, the proposed local information provision strategy outperforms global information provision strategy in system level network performance.

A dynamic taxi traffic assignment model: A two-level continuum transportation system approach

This paper proposes a two-level continuum transportation system approach to modeling a dynamic taxi traffic assignment (DTTA) problem in a dense network with real-time traffic information provision and three types of vehicles, including private cars, occupied taxis, and vacant taxis. The proposed approach treats the dense network as a continuum in the first level, in which private car and occupied taxi drivers are free to choose their paths in a two-dimensional continuous space. The proposed approach also divides the modeling region into many identical squares to form a cell-based network in the second level, in which the cells are classified into two categories: target cell with an acceptable expected rate of return (EROR) to vacant taxi drivers and non-target cell with an unacceptable EROR. The EROR associated with a cell is the ratio of the cumulative expected profit of a taxi driver who successfully picks up a customer during the customer search that starts from that cell to the sum of expected search time for this customer and expected occupied travel time to serve this customer. Based on the cell-based network, we develop a cell-based intervening opportunity model to capture the fact that vacant taxi drivers can meet a customer on the way to their destination zones and estimate the EROR. Each vacant taxi driver has a mixed strategy to determine his/her customer-search direction according to the EROR: Each vacant taxi driver in a target cell selects its neighbor cells with maximum EROR, and each vacant taxi driver in a non-target cell selects the travel time-based shortest path to his/her target cell. Meanwhile, each private car driver chooses the path that minimizes his/her own generalized travel cost, and each occupied taxi driver chooses the path that minimizes his/her customer's generalized in-vehicle travel cost. In our model, traffic density in the system is governed by the conservation law (CL), and the flow directions of different vehicles are determined by the path-choice strategies of their drivers, which are captured by Hamilton–Jacobi (HJ) equations. Both the proposed CL and HJ equations can be solved by the Lax–Friedrichs scheme, which forms the backbone of the developed solution algorithm. Finally, numerical examples and a case study are used to demonstrate the properties of the model, the performance of the solution algorithm, and the value of using our methodology for estimating network performance.