On Whether to Allow Computers to Post Traffic Advisory Messages without Operator Approval

Many of the components associated with the deployment of Intelligent Transportation Systems (ITS) to support a traffic management center (TMC) such as remote control cameras, traffic speed detectors, and variable message signs, have been available for many years. Their deployment, however, has been expensive and applied primarily to freeways and interstates, and have been deployed principally in the major metropolitan areas in the US; not smaller cities. The Knoxville (Tennessee) Transportation Planning Organization is sponsoring a project that will test the integration of several technologies to estimate near-real time traffic information data and information that could eventually be used by travelers to make better and more informed decisions related to their travel needs. The uniqueness of this demonstration is that it will seek to predict traffic conditions based on cellular phone signals already being collected by cellular communications companies. Information about the average speed on various portions of local arterials and incident identification (incident location) will be collected and compared to similar data generated by "probe vehicles". Successful validation of the speed information generated from cell phone data will allow traffic data to be generated much more economically and utilize technologies that are minimally infrastructure invasive. Furthermore, when validated, traffic information could be provided to the traveling public allowing then to make better decisions about trips. More efficient trip planning and execution can reduce congestion and associated vehicle emissions. This paper will discuss the technologies, the demonstration project, the project details, and future directions.

Don’t shoot the messenger: Traffic-irrelevant messages on variable message signs (VMSs) might not interfere with traffic management

Advanced Traffic Management Systems (ATMS) are those components of Intelligent Vehicle Highway Systems (IVHS) that integrate traffic detection, communication, and control functions to be responsive to dynamic traffic conditions and increase the efficiency of existing traffic networks. ATMS provide the management foundation that will enable and integrate other IVHS components such as Commercial Vehicle Operations, Advanced Traveler Information Systems, Advanced Vehicle Control Systems, and Advanced Public Transportation Systems. This paper defines Advanced Traffic Management Systems. It also describes the functions that may take place within an ATMS-class Traffic Management Center (TMC), a scenario that a future TMC operator may encounter, and some of the human factors issues that must be addressed in the design of an ATMS-class TMC.

In the commonwealth of Virginia, essential transportation functions such as traffic flow monitoring and incident management are remotely managed via traffic management centers (TMCs). TMCs provide the vital support for meeting the Virginia department of transportationpsilas (VDOTpsilas) goals of sustaining safe and efficient workforce mobility, commodity flow, and accessibility to critical service providers. VDOTpsilas ability to accomplish these goals may be hindered by a number of TMC problems, including security breaches and disruptive events, among others. Failure to develop plans and practices to secure and optimize TMC operations will impair VDOTpsilas ability to efficiently respond to incidents and protect lives. In an effort to maintain and optimize TMC operations, the project team developed risk management plans and policies for three case studies: (i) IT security failures, (ii) recovery in the aftermath of a disaster event, and (iii) incident responses under routine conditions.

From 3 lakh vehicles in 1985, to about 53 lakh now, vehicle population in Bangalore has increased more than five fold during the last two decades. While the vehicle population is growing by at least one lakh every year, total road length has remained the same. Many of the arterial roads and junctions are operating over the capacity and to upgrade the city's traffic management system and resulted in the creation of the Bangalore Traffic Improvement Project, B-TRAC 2010 by Bangalore City Traffic Police. The BTRAC Project was conceptualized to address traffic congestion in Bangalore by employing the latest traffic management technologies. The components of BTRAC which are a reality on ground include the state-of-the-art Traffic Management Center which is the hub of transportation management system in Bangalore, Intelligent Transport Systems, Surveillance and Enforcement Cameras, Signage, Road Markings, Street Furniture, Junction Improvements and Capacity Building. Various traffic and road safety education and training activities have also been conducted with citizens and traffic personnel throughout the city for generating awareness about traffic rules and familiarising them with the latest technologies being adapted. As a result of such consolidated traffic control and management effort, Bangalore today has a full-fledged traffic maintenance system. This has resulted in creating transparency in enforcing traffic rules and levying adequate and timely penalties. BTRAC has resulted in better management of traffic, ensuring safety and security on roads and prevention of road accidents, with the monitoring and regulation of flow of vehicles in a systematic manner. BTRAC has reduced the occurrence of road accidents in Bangalore and is creating a safe road environment for commuters. The absolute number of road accidents witnessed a growing trend till the year 2007 and then followed a declining trend over the consecutive years with 5230 cases being reported in 2013 and 5004 cases in 2014. Even the fatal accident rate is showing a declining trend over the past five years.

The widespread use of vehicle has led to new problems in the city because of the need for these vehicles in the organization of movement and parking, especially in areas characterized by increased congestion in the morning and evening peak, which is one of the problems of the great urban growth that hit the city of Baghdad. Accordingly, this study dealt with identifying the traditional and intelligent organizational means to regulate traffic, and the most important specifications and requirements for their distribution within cities. An applied case study of Maysaloun intersection in Al-Ghadeer municipality in Baghdad city was studied for identifying the streets that suffer from the lack of traditional organizational means and the imbalance in their spatial distribution. Through field surveys of the study area, it was noted that the streets suffer from congestion in the morning and evening rush hours resulting from a spatial distribution deficit and a large deficit in the number of traffic signs that have a significant role in regulating traffic and traffic management. Service levels as a street condition were compared with US HCM standards using the HCS2010 program. It was found that all streets of the study area with a service level (LOS) are lower than level (C) and therefore this condition is met by the applicability of Variable message sign (VMS). Finally, a digital photo map using a satellite image of the study area has been prepared as a proposal showing the streets where the requirements for the deployment of intelligent signs (VMS) and basic traffic signs are available to regulate and control the traffic movement and to fill the deficit and imbalance in the number and spatial distribution.

Air Pollution from vehicles become the greatest issue for several years. In these recent years carbon-dioxide emission is of about 27% from road transportation of which 55% are from passenger cars than other causes. It is common that the number of stop and go for the vehicles can emit more carbon-dioxide during traffic. In this paper, an intelligent approach is designed for managing road traffic that can reduce the number of stop and go and also waiting time in the traffic signal. This approach was established by fixing the vehicle detection sensor(IR Receiver) embedded with Zigbee transceiver at each vehicle and vehicle detection sensor(IR Transmitter) at every 250m on the road at all directions and Central Processing Unit(CPU) in traffic management centre. This CPU will detect the vehicle and its speed at every 250m, based on this information detected, the CPU will schedule the speed of the vehicle by SVM algorithm. The CPU decided speed value information will be transmitted to the respective vehicle through Zigbee transceiver. Hence every vehicle within the boundary of 500m will run at received speed level by avoiding stop and go near traffic management centre, thus reducing CO 2 emission.

Vehicular ad-hoc network (VANET) is a core component of intelligent traffic management system which could provide various of applications such as accident prediction, route reporting, etc. Due to the problems caused by traffic congestion, route reporting becomes a prospective application which can help a driver to get optimal route to save her travel time. Before enjoying the convenience of route reporting, security and privacy-preserving issues need to be concerned. In this paper, we propose a new secure and privacy-preserving route reporting scheme for VANETs. In our scheme, only an authenticated vehicle can use the route reporting service provided by the traffic management center. Further, a vehicle may receive the response from the traffic management center with low latency and without violating the privacy of the vehicle. Experiment results show that our scheme is much more efficiency than the existing one.

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s in that the historical dataset provides a pool of spatio-temporally experienced traffic conditions. It is known that traffic flow pattern spatio-temporally recurs and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. This implies that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. That is why the TMCs are reluctant to employ the black-box approaches even though there are numerous valuable research papers published. In this context, this research proposed a more readily understandable and intuitively appealing data-driven approach and developed an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

Systeme d'Information Routiere Intelligible aux Usagers (SIRIUS) is the largest urban field operational test of the advanced traveler information and automated traffic management system in Europe. With variable-message signs, SIRIUS has been in operation in the Paris region for 3 years. A preliminary investigation of the effectiveness of the SIRIUS system in traffic management is presented. The extent to which drivers respond to real-time traffic information and the consequential changes in link flow under SIRIUS is also presented. Time-series traffic data were analyzed to measure changes in mean flow rates at a selected link. It was found that variable-message signs influence drivers to choose less congested routes when drivers are provided with real-time traffic information, and that a driver's decision to divert is closely associated with the information pertaining to the level of congestion. In the Paris region, drivers received information on the length of the queue at the time of this study. As congestion becomes heavier, drivers are more likely to respond to variable-message signs. According to the data analysis, a queue length of 3 km seems to be a threshold at which a significant number of drivers choose to use an alternative route.

Evaluation of traffic management strategies for special events using probe data

Traffic congestion caused by nonrecurring incidents such as vehicle crashes and debris is a key issue for Traffic Management Centers (TMCs). Clearing incidents in a timely manner is essential to improve safety and reduce delays and emissions for the traveling public. However, TMCs and other responders face a challenge in predicting the duration of incidents (until the roadway is clear), making decisions about what resources to deploy is difficult. To address this problem, this research developed an analytical framework and end-to-end machine learning solution to predict the duration of the incident based on the information available as soon as an incident report is received. Quality predictions of incident duration can help TMCs and other responders take a proactive approach in deploying responder services such as tow trucks, and maintenance crews, or activating alternative routes. The predictions use a combination of classification and regression machine learning modules. The performance of the developed solution has been evaluated based on the Mean Absolute Error (MAE), or deviation from the actual incident duration as well as Area Under the Curve (AUC) and Mean Absolute Percentage Error (MAPE). The results showed that the framework significantly improved the prediction of incident duration compared to previous research methods.

The three major elements of TravTek are the TravTek Information and Services Center (TISC), the TravTek vehicles and the Traffic Management Center (TMC). The Federal Highway Administration (FHWA) has issued a contract to design and implement the TMC and a network of agencies and companies that will supply traffic information and data. These sources of information and the TravTek vehicles are known as the Traffic Information Network (TIN). The TIN sends this information to the TMC which combines and processes it to generate reports in a process known as data fusion. Historical data and operator-entered information are also included in the data fusion process to generate the various status, incident, and link data reports. The TMC facilitates and pursues incident and congestion confirmation and clearance information from the TIN sources, fuses the information and provides it in the form of a computerized report to various users. Only data pertaining to link travel times and confirmed incidents or congestion are provided to the TravTek vehicles and to other end users.

Traffic violation reporting schemes for fog-assisted vehicular ad hoc networks are generally designed to support traffic management centers (TMCs) in detecting traffic violations so that appropriate actions can be taken against the involved vehicle owners. However, there are ongoing challenges such as ensuring accuracy or accountability with minimal privacy dis closure (e.g., accurate and reliable detection of traffic violations without disclosing the contents of the incident, achieving identity authentication while protecting the privacy of reporters), and how to guarantee the reports are correctly processed complicate the design of such schemes. To address these challenges, we propose a threshold anonymous traffic violation reporting (TATVR) scheme under the assumptions that the fog nodes (i.e., roadside units) and the TMC are semi-trusted, and the number of colluding vehicles is limited. We then extend the TATVR scheme (i.e., extended TATVR or E-TATVR) that does not rely on these assumptions. We explain how TMC can process the received reports more efficiently using the proposed E-TATVR scheme. We also evaluate the security of both proposed schemes and demonstrate that they simultaneously support (strong) confidentiality, non-frameability, conditional unlinkability, unforgeability, and conditional anonymity. In particular, we show that both schemes guarantee strong confidentiality, and the E-TATVR scheme additionally supports report traceability.

Fuzzy Power Heronian function based CoCoSo method for the advantage prioritization of autonomous vehicles in real-time traffic management