Traffic-related Information Research Articles

SoVEC: Social vehicular edge computing-based optimum route selection

This paper proposes a new architecture Social Vehicular Edge Computing (SoVEC) by integrating three domains: social network, vehicular ad-hoc network, and mobile edge computing. The users access various mobile applications and share various types of information on the social network during travel time. Using SoVEC three categories of social networks are generated based on the type of information shared among the users such as traffic information, professional information, and personal interests. To reach the destination in minimal time, this paper proposes an optimum route selection strategy based on TOPSIS method and genetic algorithm. The SoVEC is simulated using the network simulator Qualnet 7, and average delay, jitter, and throughput are determined. A case study of generating social network based on road traffic-related information is also demonstrated. Finally, the effectiveness of the proposed approach for selecting the optimum route is assessed, and the results present that the proposed method outperforms the existing algorithms.

Enhancing Traffic Management in Smart Cities: A Cyber-Physical Approach

The challenges of managing traffic in rapidly urbanizing cities with increasing populations. It highlights the need for advanced technologies and frameworks in smart cities to develop intelligent traffic management systems. The proposed approach is a cyber-physical framework that integrates physical infrastructure with digital infrastructure for real-time data collection, analysis, and decision-making. By using sensors, IoT devices, and data analytics, the system captures and processes traffic-related information to predict traffic flow, optimize signal timings, and recommend alternate routes. The system also communicates with connected vehicles to provide real-time updates and alerts, improving driver safety and reducing congestion. The cyber-physical approach allows seamless integration with other smart city systems for efficient coordination and resource allocation. Implementing this system offers benefits such as reduced travel times, improved air quality, enhanced road safety, and increased overall efficiency in urban transportation. It can adapt and evolve by learning from new data inputs and incorporating emerging technologies, ensuring sustainability and scalability. Overall, this paper presents a comprehensive framework for enhancing traffic management in smart cities, transforming urban transportation, and improving residents' quality of life while making cities more sustainable and resilient.

A Security-Enhanced Certificateless Conditional Privacy-Preserving Authentication Scheme for Vehicular Ad Hoc Networks

By adopting advanced Internet of Things (IoT) technology to sense and collect traffic-related information to improve traffic safety and efficiency, the vehicular ad hoc network (VANET) is becoming a prominent application that changes human driving experiences in the current era. Because frequent data exchange occurs in open environments, VANETs are inherently vulnerable to security and privacy attacks. In history, many certificateless aggregate signature (CLAS) schemes with conditional privacy-preserving (CPP) have been proposed to ensure the authenticity and integrity of the exchanged data and protect users’ privacy. However, we reveal that the state-of-the-art schemes cannot be deployed in practical VANET applications by proposing concrete signature forgery attacks. To this end, we propose a new CLAS-based authentication scheme with CPP for VANETs. The rigorous security proofs based on the standard cryptographic assumption show that the scheme has enhanced security. Moreover, theoretical analysis and experimental evaluation illustrate the practicality of our design.

Mobility Data for a Safer and Greener Transport

Traffic network operators are using several channels to share their traffic related data, on one hand because of legal obligation, on the other hand to ensure a higher level of service for their customers. Of course, the value chain, and the provided content will change a lot with the emerge of connected and automated driving, or with the spread of Mobility-as-a-Service solutions and Alternative Fuel Infrastructure Regulation (AFIR) related data sharing in the near future. This paper gives a comprehensive overview of the current practice in Hungary, introducing the ways of distributing multimodal traffic related information, some existing services that were implemented based on these automatic data channels. The paper also gives an overview on the ongoing transition towards automatic data provision and how the regular road operators’ role change to digital road operators, furthermore the European harmonisation process that was started in the framework of the Napcore project.

A Crowd-Aided Vehicular Hybrid Sensing Framework for Intelligent Transportation Systems

The development of Internet of Things (IoT) techniques enables the paradigm shift in traffic data collecting. In traditional practices of transportation system's constructions, traffic-related information is collected based on dedicated sensor networks, which are not only coverage-limited but also cost-consuming. With the enrichment of the concepts concerning “social sensors” and “social transportation”, Sparse mobile crowdsensing (MCS) emerges as a promising sensing paradigm to collect data from only a few subareas by recruiting vehicles or mobile users with portable devices and to infer the data in unsensed subareas with acceptable errors at a low-cost manner. However, in real-world sensing campaigns, the Sparse MCS systems often fail to collect data from any subareas of interest since the assumption about sufficient participants is not always realistic. To be specific, the recruitment of participants is often limited by interest deficiency, privacy awareness, and distribution biases. To handle this problem, we introduce the dedicated sensing vehicles (DSVs) e.g., drones or driverless vehicles into traditional Sparse MCS to improve subarea coverage and inference performance. To achieve effective collaboration among DSVs and mobile users, we first design a crowd-aided vehicular hybrid sensing framework, which defines the order of task assignment for DSVs and mobile users as well as the budget allocation. In terms of DSVs route planning, we propose a three-step strategy, including optimal route searching, fused route selection, and final route determination. Moreover, mobile users are selected based on a proposed novel selection strategy. Experimental findings on two real-world datasets validate the effectiveness (with less inference error) of the hybrid sensing framework and the proposed strategies, in comparison with the user-only/DSV-only framework and five baselines. Results reveal important implications of applying the hybrid sensing paradigm in intelligent transportation systems to enhance data collection.

Cyber Security Issues and Solution in Vehicular Networks

Vehicle Ad-hoc Networks (VANETs), which share traffic-related information between cars and infrastructure and aid to enhance driver safety and road safety, are capable of and viable for implementing Intelligent Transportation Systems (ITS). efficient traffic flow. Nonetheless, the confidentiality and privacy of such information is crucial for VANET due to the open wireless access channel. Attackers can compromise VANET security by capturing, intercepting, altering, replaying, or deleting traffic-related information. Thus, the hottest study field right now is assuring the security and privacy of traffic-related information in VANETs. Vehicle communication has been the subject of extensive effort in this situation. Yet, in terms of security needs, security assaults, and performance effectiveness, these duties fall short of adequately addressing security challenges.This white paper organises various authentication and privacy systems into categories and analyses their workings, advantages and disadvantages, security needs, threats, and performance metrics. Lastly, we chose the research inquiries that would be posted in the VANET security area.

Blockchain-Envisioned Provably Secure Multivariate Identity-Based Multi-Signature Scheme for Internet of Vehicles Environment

The deployed vehicles in an Internet of Vehicles (IoV) can take intelligent decisions by means of exchanging the real-time traffic-related information between the vehicles and IoV infrastructures. This further reduces the probability of the traffic jams and accidents. However, the insecure (public) communication among the various entities in IoV makes various security threats and attacks that can be launched by passive/active adversaries present in the network. In view of this context, there is a need of an efficient cryptographic primitive which can produce single compact signature. A multi-signature scheme (MSS) empowers a collection of signers to conjointly sign a given message using a single compact signature that can be verified by any verifier. Herein, we put forward a new identity-based multivariate MSS, namely <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MV-MSS</monospace> , which is built on top of the intractability of multivariate-quadratic (MQ) problem. The fact is that multivariate public key cryptosystem provides fast, post-quantum safe and efficient primitives, which makes it the front runner candidate among the post-quantum cryptographic candidates. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MV-MSS</monospace> is proven to be secure in the existential unforgeability under chosen-message and chosen identity attack model if solving the MQ problem is NP-hard. We then incorporate the designed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MV-MSS</monospace> in IoV application where the leader (cluster head) selected from a group of vehicles in a dynamic cluster forms the multi-signatures on the messages securely received from its member vehicles. Later, the messages along with their multi-signatures are forwarded to the nearby road-side unit (RSU) of the cluster head, which are then forwarded to a cloud server in the blockchain center maintained by a Peer-to-Peer (P2P) cloud servers network. In this way, the messages and their signatures considered as transactions are put in blocks and added into a public blockchain with the help of consensus algorithm. A comparative study among the proposed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MV-MSS</monospace> and other existing schemes shows that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MV-MSS</monospace> is efficient and secure as compared to other schemes. Finally, a blockchain implementation through simulation study has been performed to show its practical use in IoV application.

Blockchain-based vehicular ad-hoc networks: A comprehensive survey

Vehicular ad-hoc networks (VANETs) are increasingly commonplace, partly due to the popularity of electric vehicles and the digitalization of cities. Data collected and shared in VANETs include traffic-related information, such as those relating to real-time traffic situations and road works. In recent times, there has been a trend of moving away from a centralized approach to a decentralized approach, for example, using Blockchain to facilitate secure data sharing and traceability of critical information. Hence, in this paper, we comprehensively survey the existing literature on blockchain-based VANET systems, focusing on the application of different blockchain technologies in different contexts, as well as the associated challenges and research opportunities.

Traffic and road conditions monitoring system using extracted information from Twitter

Congested roads and daily traffic jams cause traffic disturbances. A traffic monitoring system using closed-circuit television (CCTV) has been implemented, but the information gathered is still limited for public use. This research focuses on utilizing Twitter data to monitor traffic and road conditions. Traffic-related information is extracted from social media using text mining approach. The methods include Tweet classification for filtering relevant data, location information extraction, and geocoding in order to convert text-based location into coordinate information that can be deployed into Geographic Information System. We test several supervised classification algorithms in this study, i.e., Naïve Bayes, Random Forest, Logistic Regression, and Support Vector Machine. We experiment with Bag Of Words (BOW) and Term Frequency - Inverse Document Frequency (TF-IDF) as the feature representation. The location information is extracted using Named Entity Recognition (NER) and Part-Of-Speech (POS) Tagger. The geocoding is implemented using the ArcPy library. The best model for Tweet relevance classification is the Logistic Regression classifier with the feature combination of unigram and char n-gram, achieving an F1-score of 93%. The NER-based location extractor obtains an F1-score of 54% with a precision of 96%. The geocoding success rate for extracting the location information is 68%. In addition, a web-based visualization is also implemented in order to display traffic information using the spatial interface.

Mapping Undermined Role of Information and Communication Technologies in Floods

Abstract This paper reports the undermined potential of broad range of (Information and communication technologies) ICTs that remained effective yet unnoticed in different flood-phases to exchange traffic, travel, and evacuation related information. The objective was to identify convenient ICTs that people found operational in life cycle of a flood. For the purpose, ICTs were tested in relation to 18 different variables based on personal capabilities, demographic, and vehicle-based information etc. Samples of 105 and 102 subjects were recruited from flood-prone communities of developing and developed case-studies respectively, through random sampling and analyzed through Multinomial Logistic Regression. Those categories of independent variables that showed p-value ≥ 0.05 were considered to model the results. The main findings showed that in developed countries TV, mobile phone subscriptions and international news channels were prominent source of information whilst in developing countries multiple messengers, Facebook and contributory websites were impactful for information dissemination. The results are useful for academia, engineers, and policy makers and for future work same variables can be tested for different disaster affected communities.

On the (In)Security of 1090ES and UAT978 Mobile Cockpit Information Systems–An Attacker Perspective on the Availability of ADS-B Safety- and Mission-Critical Systems

Automatic dependent surveillance-broadcast (ADS-B) is a key air surveillance technology and a critical component of next-generation air transportation systems. It significantly simplifies aircraft surveillance technology and improves airborne traffic situational awareness. Many types of mobile cockpit information systems (MCISs) are based on ADS-B technology. MCIS gives pilots the flight and traffic-related information they need. MCIS has two parts: an ADS-B transceiver and an electronic flight bag (EFB) application. The ADS-B transceivers transmit and receive the ADS-B radio signals while the EFB applications hosted on mobile phones display the data. Because they are cheap, lightweight, and easy to install, MCISs became very popular. However, due to the lack of basic security measures, ADS-B technology is vulnerable to cyberattacks, which makes the MCIS inherently exposed to attacks. Attacks are even more likely for the MCIS, because they are power, memory, and computationally constrained. This study explores the cybersecurity posture of various MCIS setups for both types of ADS-B technology: 1090ES and UAT978. Total six portable MCIS devices and 21 EFB applications were tested against radio-link-based attacks by transmission-capable software-defined radio (SDR). Packet-level denial of service (DoS) attacks affected approximately 63% and 37% of 1090ES and UAT978 setups, respectively, while many of them experienced a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">system crash</i> . Our experiments show that DoS attacks on the reception could meaningfully reduce transmission capacity. Our coordinated attack and fuzz tests also reported worrying issues on the MCIS. The consistency of our results on a very broad range of hardware and software configurations indicate the reliability of our proposed methodology as well as the effectiveness and efficiency of our platform.

SEE-TREND: SEcurE Traffic-Related EveNt Detection in Smart Communities.

It has been widely recognized that one of the critical services provided by Smart Cities and Smart Communities is Smart Mobility. This paper lays the theoretical foundations of SEE-TREND, a system for Secure Early Traffic-Related EveNt Detection in Smart Cities and Smart Communities. SEE-TREND promotes Smart Mobility by implementing an anonymous, probabilistic collection of traffic-related data from passing vehicles. The collected data are then aggregated and used by its inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic-related information along the roadway to help the driving public make informed decisions about their travel plans, thereby preventing congestion altogether or mitigating its nefarious effects.

SDN-Enabled Adaptive Broadcast Timer for Data Dissemination in Vehicular Ad Hoc Networks

This paper presents an adaptive broadcast interval for data dissemination for an infrastructure-to-vehicle (I2V) environment. The roadside units (RSUs) disseminate the data to the vehicles after a specified broadcast time interval. Time interval tuning for the data dissemination is one of the parameters used to determine the effectiveness of the broadcast time interval at RSU for safety and infotainment message dissemination to the vehicles under a particular RSU coverage. We formulated the problem of computing an optimal broadcast time interval to maximize the service ratio, reducing the duplicate message overhead. The proposed scheme uses the software-defined network (SDN) architecture having OpenFlow-enabled RSUs and SDN controllers. Based on the global view of traffic-related information, the proposed SDN controller computes the broadcast time interval for data dissemination among the vehicles. We used the NS-3 simulator to evaluate the proposed scheme's performance by generating a real scenario of VANET traffic using SUMO. Simulation results show that the proposed SDN-based scheme increases the average service ratio by 15-20% and reduces duplicate message overhead by 7-10% in comparison to the other existing state-of-the-art schemes.

Space-weighted information fusion using deep reinforcement learning: The context of tactical control of lane-changing autonomous vehicles and connectivity range assessment

The connectivity aspect of connected autonomous vehicles (CAV) is beneficial because it facilitates dissemination of traffic-related information to vehicles through Vehicle-to-External (V2X) communication. Onboard sensing equipment including LiDAR and camera can reasonably characterize the traffic environment in the immediate locality of the CAV. However, their performance is limited by their sensor range (SR). On the other hand, longer-range information is helpful for characterizing imminent conditions downstream. By contemporaneously coalescing the short- and long-range information, the CAV can construct comprehensively its surrounding environment and thereby facilitate informed, safe, and effective movement planning in the short-term (local decisions including lane change) and long-term (route choice). Current literature provides useful information on CAV control approaches that use only local information sensed from the proximate traffic environment but relatively little guidance on how to fuse this information with that obtained from downstream sources and from different time stamps, and how to use the fused information to enhance CAV movements. In this paper, we describe a Deep Reinforcement Learning based approach that integrates the data collected through sensing and connectivity capabilities from other vehicles located in the proximity of the CAV and from those located further downstream, and we use the fused data to guide lane changing, a specific context of CAV operations. In addition, recognizing the importance of the connectivity range (CR) to the performance of not only the algorithm but also of the vehicle in the actual driving environment, the study carried out a case study. The case study demonstrates the application of the proposed algorithm and duly identifies the appropriate CR for each level of prevailing traffic density. It is expected that implementation of the algorithm in CAVs can enhance the safety and mobility associated with CAV driving operations. From a general perspective, its implementation can provide guidance to connectivity equipment manufacturers and CAV operators, regarding the default CR settings for CAVs or the recommended CR setting in a given traffic environment.

A Multimetric Predictive ANN-Based Routing Protocol for Vehicular Ad Hoc Networks

Vehicular networks support intelligent transportation system (ITS) to improve drivers' safety and traffic efficiency on the road by exchanging traffic-related information between vehicles and also between vehicles and infrastructure. Routing protocols that are designed for vehicular networks should be flexible and able to adapt to the inherent dynamic network characteristics of these kind of networks. Therefore, there is a need to have effective vehicular communications, not only to make mobility more efficient but also to reduce collateral issues such as pollution and health problems. Nowadays, the use of machine learning (ML) algorithms in wireless networks are on the rise, including vehicle networks that can benefit from possible data-driven predictions. This work aims to contribute to the design of a smart ML-based routing protocol for vehicular ad hoc networks (VANETs) used to report traffic-related messages in urban environments. We propose a new ML-based forwarding algorithm to be used by the current vehicle holding a given packet to predict which vehicle within its transmission range is the best next-hop to forward that packet towards its destination. Our algorithm is based on a neural network designed from a dataset that contains data records that are captured during simulated urban scenarios. Simulation results show how our ML-based proposal improves the performance of our multimetric routing protocol for VANETs in urban scenarios in terms of packet delivery probability. The performance evaluation of MPANN shows packet losses lower than 20% (and average packet delays below 0.04 ms) for different vehicles' densities, in completely new scenarios but of similar complexity than the Barcelona scenario used to train the model. Even for much more complex scenarios (with narrow curvy streets), our proposal is able to reduce the packet losses in 20% with respect to the multimetric routing protocol as well as the average packet delays in 0.04 ms.

Traffic Prediction Using Multifaceted Techniques: A Survey

Road transportation is the largest and complex nonlinear entity of the traffic management system. Accurate prediction of traffic-related information is necessary for an effective functioning of Intelligent Transportation System (ITS). It is still a challenge for the departments of transportation to choose an appropriate prediction technique for the ITS applications. That is, a user must be able to utilize the disseminated information effectively by the forecasting models. This paper provides a detailed survey of the latest forecasting technologies and contributes to understand the key concept behind the prediction approaches. To provide guidelines to the decision-maker, this paper reviews multifaceted techniques developed by various authors for traffic prediction. We start classifying each technique into four categories namely, Machine Learning (ML), Computational Intelligence (CI), Deep Learning (DL), and hybrid algorithms. Many have conducted survey using model-driven or data-driven methods. We are the first to explore the area of traffic prediction based on the advances in multifaceted techniques proposing algorithmic approaches for key traffic characteristics in the forecasting process. The role of dependent factors in the prediction are analyzed thoroughly. We have analyzed each algorithm chronologically based on various traffic traits. The approaches are summarized based on the rational usage and performance of each technique. The analysis led to several research queries, and the appropriate responses are provided based on our detail survey. Finally, it is confirmed that currently, CI-MLs and DL hybrid techniques outperforms the rest in the field of traffic prediction. Ultimately suggested open challenges and future direction to explore the capability of DL and hybrid techniques further in the field of traffic prediction.

Link reliability-based multi-hop directional location routing in vehicular ad hoc network

The vehicular ad hoc network (VANET) is a self-organized and decentralized wireless ad hoc network formed by the mobile vehicles on demand to communicate the traffic-related valuable information among the vehicles. The mobile vehicles work as a node because they use to either send or receive traffic-related information in the network. The vehicles can communicate within restricted distance but mobility nature causes they frequently switch from one network to another network topology; therefore, the links among the vehicles seldom vanish. Therefore, in the multi-hop VANET delivery of the messages at the anticipated destination is problematic. In this paper, a routing mechanism namely link reliability-based multi-hop directional location routing (MHDLR) has proposed to overcome the link vanish problem. The MHDLR decides a link reliability-based next-hop node to create a strongly durable path from the source to destination. In multi-hop VANET, the inter-vehicle distance directly impacts on the connectivity of the vehicles. Therefore, to estimate the link reliability of vehicles their inter-vehicle distance and relative velocity have considered. The geometry-based localization mechanism (G-BLM) has been proposed to estimate the inter-vehicle distance of the vehicles. The performance of MHDLR has estimated for routing metrics i.e. path vanish, message broadcasting time in the group, packet delivery ratio and throughput. The simulated outcomes of the proposed model MHDLR have shown, its performance is better as compared to the existing fuzzy-logic based directional location routing (FLDLR), directional location aided routing (D-LAR) and location aided routing (LAR) protocols.

Inter-vehicle distance-based location aware multi-hop routing in vehicular ad-hoc network

The vehicular ad-hoc network (VANET) is a self-organized wireless ad-hoc network created by movable vehicles of the limited transmission range R to transmit traffic-related information to nearby vehicles on demand. It is worthy to mention that in VANET the vehicles are the source of transmission of data packets so that it works as a node. Vehicles restricted transmission range causes the route to participate in data transmission from the source S to destination D may vanish seldom. Therefore, a stable route from source to destination needs to deliver data packets at the intended destination. This paper presented an inter-vehicle distance-based location-aware multi-hop routing (LAMHR) in the vehicular ad-hoc network to enhance the vehicle's connectivity. LAMR predicts the future location of the nodes to select an optimal next forwarder towards the destination to establish a stable route from source to destination. In this paper, to obtain the inter-vehicle distance, a geometry-based localization technique has developed that impact on the vehicle's connectivity. Performance of the LAMHR has evaluated in terms of the path vanish, node broadcasting time, packet delivery ratio and throughput. Through the simulated results, it has shown that the proposed LAMHR model gives high performance comparatively the existing fuzzy logic-based directional location routing (FLDLR), directional-location aided routing (D-LAR) and location aided routing (LAR) protocols.

AN EFFICIENT INTELLIGENT TRAFFIC LIGHT CONTROL AND DEVIATION SYSTEM FOR TRAFFIC CONGESTION AVOIDANCE USING MULTI-AGENT SYSTEM

An efficient and intelligent road traffic management system is the corner stone for every smart cities. Vehicular Ad-hoc NETworks (VANETs) applies the principles of mobile ad hoc networks in a wireless network for Vehicle-to-vehicle data exchange communication. VANETs supports in providing an efficient Intelligent Transportation System (ITS) for smart cities. Road traffic congestion is a most common problem faced by many of the metropolitan cities all over the world. Traffic on the road networks are widely increasing at a larger rate and the current traffic management systems is unable to tackle this impediment. In this paper, we propose an Efficient Intelligent Traffic Light Control and Deviation (EITLCD) system, which is based on multi-agent system. This proposed system overcomes the difficulties of the existing traffic management systems and avoids the traffic congestion problem compare to the prior scenario. The proposed system is composed of two systems: Traffic Light Controller (TLC) system and Traffic Light Deviation (TLD) system. The TLC system uses three agents to supervise and control the traffic parameters. TLD system deviate the vehicles before entering into congested road. Traffic and travel related information from several sensors are collected through a VANET environment to be processed by the proposed technique. The proposed structure comprises of TLC system and makes use of vehicle measurement, which is feed as input to the TLD system in a wireless network. For route pattern identification, any traditional city map can be converted to planar graph using Euler’s path approach. The proposed system is validated using Nagel–Schreckenberg model and the performance of the proposed system is proved to be better than the existing systems in terms of its time, cost, expense, maintenance and performance.

Better safe than sorry: a vehicular traffic re-routing based on traffic conditions and public safety issues

Vehicular traffic re-routing is the key to provide better traffic mobility. However, taking into account just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of proper transportation management. In this way, context-aware and multi-objective re-routing approaches will play an important role in traffic management. Yet, most procedures are deterministic and cannot support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, consequently degrading overall traffic efficiency. So, we propose an efficient algorithm named as Better Safe Than Sorry (BSTS), based on Pareto-efficiency. Simulation results have shown that our proposal provides a better trade-off between mobility and safety than state-of-the-art approaches and also avoids the problem of potentially creating different congestion spots.