Analyzing the Real Time Factors: Which Causing the Traffic Congestions and Proposing the Solution for Pakistani City

AbstractVehicle ownerships integral part of modern life and traffic congestion an unavoidable inconvenience. The Western countries have a far better control on the pace of number of vehicles on a road matched with supporting infrastructure. In contrast, cash strapped underdeveloped countries have a poorly built and scarce number of main roads with problems compounded by soft car loans, leases and other discounts. As a result several developing countries have been inundated with peripheral complications such as pollution and congestion undermining their economy with enormous energy bills negatively impacting respective economy. Case in point is Pakistan, where depilating infrastructure or absence outright thereof and ever more number of vehicles on the road presents a unique and highly complicated problem. One can term traffic in Sub-Continent as controlled chaos and we plan to develop an organized solution from the chaos. This presents a unique challenge in traffic management. We have developed a smart phone application when the phone is placed in vehicles, provides data for the origin and destination routes. Taking 6 parameters, which we believe mostly impacts the destination arrival time for the driver in Pakistan we propose to develop a model supported by empirical data that will enable driver to select weather they are interested in economy of fuel or economy of time in reaching their destination. We propose to plot time it takes to reach destination versus the 6 factors that determines destination arrival time. The curve will be generated for each route and from the graph median time, standard deviation as well as confidence interval will be computed. Large data will be collected and statistical analysis will be performed to verify the integrity of the model

On the design and implementation of an on-board test bed system for V2V road hazard signaling

This paper describes the design, implementation, and testing of an ITS-G5 prototype Road hazard Signaling (RHS) system that is inspired by the concept of crowdsourcing. Our approach enables drivers to interact with a touchscreen onboard interface to send ITS-G5 decentralized environmental notification messages (DENM) in order to warn nearby vehicles against the presence of a hazardous situation. These messages are analyzed, filtered for relevance, and presented to concerned drivers via the Onboard Units (OBUs) so that precautionary measures can be taken. We describe the design and implementation aspects of the proposed system and update the open source cargeo6 implementation of the ITS GeoNetworking protocol stack. We successfully implemented and validated the prototype system using an indoor testbed and carried various performance analysis experiments

Empowering Communications in Vehicular Networkswith an Intelligent Blockchain-Based Solution

Blockchains have emerged over time as a reliable and secure way to record transactions in an immutable manner in a wide range of application domains. However, current related solutions are not yet capable of appropriately checking the authenticity of data when their volumes are huge. They are not also capable of updating Blockchain data blocks and synchronizing them within reasonable timeframes. This is the case within the specific context of Blockchain vehicular networks, where these solutions are commonly cumbersome when attempting to add new vehicles to the network. In order to address these problems, we propose in this paper a new Blockchain-based solution that intelligently implement selective communication and collaborative endorsement approaches to reduce communications between vehicles. Our solution represents the vehicles of the Blockchain as intelligent software agents with a Belief-Desire-Intention (BDI) architecture. Furthermore, we propose an approach based on multi-endorsement levels to exchange data of varying sensitive categories. This approach, which is based on endorsing scores, is also used to shorten the admission of new vehicles into the Blockchain. We run simulations using the Hyperledger Fabric Blockchain tool. Results show the efficiency of our solution in reducing the processing times of transactions within two different scenarios

The covcrav project: Architecture and design of a cooperative v2v crash avoidance system

© 2019 The Authors. Published by Elsevier B.V. Systems capable of warning motorists against hazardous driving conditions are extremely useful for next-generation cooperative situational awareness and collision avoidance systems. In this paper, we present some preliminary results related to the COVCRAV project which aims to develop an on-board Road Hazard Signaling (RHS) system based on a crowd-apprising model. Unlike other approaches that rely on the automatic detection of dangerous situations via onboard sensors or warning messages received from roadside units, our approach enables drivers to interact directly with a touchscreen Driver Vehicle Interface (DVI) to notify nearby vehicles about the presence of a hazardous driving situation based on many high-value safety use-cases. We describe our RHS application and highlight the key functions provided by the originating and the receiving ITS applications. We also provide some details regarding the design aspects and system architecture of the proposed system

ConVeh: Driving Safely into a Connected Future

© 2017 The Authors. Published by Elsevier B.V. The loss of lives and damages to the property due to the vehicle crashes and road accidents have been an issue for long; a quarter of these accidents happen due to the adverse weather conditions. This paper presents the idea of cooperative driving technique for the drivers with the use of Connected Vehicles to minimize road accidents, traffic congestions, and to lessen, as far as possible, the effects of traffic on the environment and the loss of lives and economy. The frameworks for improving situational awareness and crash avoidance suggested hereby are vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) data transmission systems. The research will primarily focus on the feasibility of CVs as applicable to the contemporary physical and virtual infrastructure and suggest the required adaptations, while the technical needs for the effective and successful implementation of a robust communication framework through the use of dedicated short-range communications (DSRC) will be discussed thereafter. Peer-review under responsibility of the Conference Program Chairs

A Neural network approach to visibility range estimation under foggy weather conditions

© 2017 The Authors. Published by Elsevier B.V. The degradation of visibility due to foggy weather conditions is a common trigger for road accidents and, as a result, there has been a growing interest to develop intelligent fog detection and visibility range estimation systems. In this contribution, we provide a brief overview of the state-of-the-art contributions in relation to estimating visibility distance under foggy weather conditions. We then present a neural network approach for estimating visibility distances using a camera that can be fixed to a roadside unit (RSU) or mounted onboard a moving vehicle. We evaluate the proposed solution using a diverse set of images under various fog density scenarios. Our approach shows very promising results that outperform the classical method of estimating the maximum distance at which a selected target can be seen. The originality of the approach stems from the usage of a single camera and a neural network learning phase based on a hybrid global feature descriptor. The proposed method can be applied to support next-generation cooperative hazard & incident warning systems based on I2V, I2I and V2V communications. Peer-review under responsibility of the Conference Program Chairs

Evaluating active traffc management (ATM) strategies under non-recurring congestion: Simulation-based with benefit cost analysis case study

© 2020 by the authors. Dynamic hard shoulder running and ramp closure are two active traffic management (ATM) strategies that are used to alleviate highway traffic congestion. This study aims to evaluate the effects of these two strategies on congested freeways under non-recurring congestion. The study\u27s efforts can be considered in two parts. First, we performed a detailed microsimulation analysis to quantify the potential benefits of these two ATM strategies in terms of safety, traffic operation, and environmental impact. Second, we evaluated the implementation feasibility of these two strategies. The simulation results indicated that the implementation of the hard shoulder showed a 50%-57% reduction in delay, a 41%-44% reduction in fuel consumption and emissions, and a 15%-18% increase in bottleneck throughput. By contrast, the implementation of ramp closure showed a 20%-34% decrease in travel time, a 6%-9% increase in bottleneck throughput, and an 18%-32% reduction in fuel consumption and emissions. Eventually, both strategies were found to be economically feasible

Autonomous Driving and Connected Mobility Modeling: Smart Dynamic Traffic Monitoring and Enforcement System for Connected and Autonomous Mobility

In recent years, autonomous vehicles (AVs), connected vehicles (CVs) and all relative technology have been in the spotlight, being intensively researched and developed. There is high anticipation on the benefits of automation and the overall reform it will bring to the transport sector, with some optimistic estimates considering it as a reality within the next few years. Evidently, AVs and CVs are attracting considerable attention and are developed very rapidly, cultivating great expectations for traffic safety improvements. While their potential is enormous and undeniable, benefits are not automatically guaranteed as there are parameters that currently appear unforeseen. This paper investigates the ways that monitoring and enforcement of autonomous vehicles can be improved and serious problems such as tailgating and crashes can be mitigated. This paper\u27s result could provide useful conclusions about human factor, the effectiveness of existing monitoring and enforcing systems and possible future systems regarding enforcement and monitoring of autonomous vehicles (AVs)

Estimating ambient visibility in the presence of fog: a deep convolutional neural network approach

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Next-generation intelligent transportation systems are based on the acquisition of ambient data that influence traffic flow and safety. Among these, is the ambient visibility range whose estimation, in the presence of fog, is extremely useful for next-generation intelligent transportation systems. However, existing camera-based approaches are based on “engineered features” extraction methods that use computer algorithms and procedures from the image processing field. In this contribution, a novel approach to estimate visibility range under foggy weather conditions is proposed which is based on “learned features” instead. More precisely, we use AlexNet deep convolutional neural network (DCNN), trained with raw image data, for feature extraction and a support vector machine (SVM) for visibility range estimation. Our quantitative analysis showed that the proposed approach is very promising in estimating the visibility range with very good accuracy. The proposed solution can pave the way towards intelligent driveway assistance systems to enhance awareness of driving weather conditions and hence mitigate the safety risks emanating from fog-induced low visibility conditions