Real-time Traffic Information Research Articles

Evaluating Link Lifetime Prediction to Support Computational Offloading Decision in VANETs.

In urban mobility, Vehicular Ad Hoc Networks (VANETs) provide a variety of intelligent applications. By enhancing automobile traffic management, these technologies enable advancements in safety and help decrease the frequency of accidents. The transportation system can now follow the development and growth of cities without sacrificing the quality and organisation of its services thanks to safety apps that include collision alerts, real-time traffic information, and safe driving applications, among others. Applications can occasionally demand a lot of computing power, making their processing impractical for cars with limited onboard processing capacity. Offloading of computation is encouraged by such a restriction. However, because vehicle mobility operations are dynamic, communication times (also known as link lifetimes) between nodes are frequently short. VANET applications and processes are impacted by such communication delays (e.g., the offloading decision when using the Computational Offloading technique). Making an accurate prediction of the link lifespan between vehicles is therefore challenging. The effectiveness of the communication time estimation is currently constrained by the link lifespan prediction methods used in the computational offloading process. This work investigates five machine learning (ML) algorithms to predict the link lifetime between nodes in VANETs in different scenarios. We propose the procedures required to carry out the link lifetime prediction method using existing ML techniques. The tactic creates datasets with the features the models need to learn and be trained. The SVR and XGBoost algorithms that were selected as part of the assessment process were trained. To make the prediction using the trained models, we modified the lifespan prediction function from an offloading approach. To determine the viability of applying link lifespan predictions from the models trained in the road and urban scenarios, we conducted a performance study. The findings indicate that compared to the conventional prediction strategy described in the literature, the suggested link lifetime prediction via regression approaches decreases prediction error rates. An offloading method from the literature is extended by the selected SVR. The task loss and recovery rates might be significantly reduced using the SVR. XGBoost outperformed its ML competitors in task recovery or drop rate by 70% to 80% in an assessed hypothesis compared to an offloading choice technique in the literature. With greater offloading rates from an application on the VANET, this effort is intended to give better efficiency in estimating this data using machine learning in various vehicular settings.

SGXAP: SGX-Based Authentication Protocol in IoV-Enabled Fog Computing

With the maturity and popularization of the Internet of Things, we saw the emergence of the Internet of Vehicles. This collects and processes real-time traffic information, alleviates traffic congestion, and realizes intelligent transportation. However, sensitive information, such as real-time driving data of vehicles, are transmitted on public channels, which are easily to steal and manipulate for attackers. In addition, vehicle communications are vulnerable to malicious attacks. Therefore, it is essential to design secure and efficient protocols. Many studies have adopted asymmetric cryptosystems and fog computing to in this environment, but most of them do not reflect the advantages of fog nodes, which share the computational burden of cloud servers. Therefore, it is challenging to design a protocol that effectively uses fog nodes. In this paper, we design an authentication protocol based on a symmetric encryption algorithm and fog computing in the Internet of Vehicles. In this protocol, we first propose a four-layer architecture that significantly reduces the computational burden of cloud servers. To resist several well-known attacks, we also apply Intel software guard extensions to our protocol. This is because it can resist privileged insider attacks. We prove the security of the proposed protocol through the Real-Or-Random model and informal analysis. We also compare the performance of the proposed protocol with recent protocols. The results show better security and a lower computational cost.

Collision Avoidance Predictive Motion Planning Based on Integrated Perception and V2V Communication

Autonomous vehicles (AVs), as one of the cores in future intelligent transportation systems (ITSs), can facilitate reliable and safe traffic operations and services. The ability to automatically perform effective AV motion planning and deploy efficient perception systems is vital for advancing the quality of core transportation services. However, existing research studies have only considered the applications of either of these approaches, which neglect their necessary interactions in real-world AV motion planning systems. To address this problem, we design an AV motion planning strategy based on motion prediction and V2V communication. Specifically, we propose the perception system and V2V communication module to provide real-time traffic and vehicular information to the participated AVs. Then, we formulate the AV lane-change motion planning problem through the scope of model predictive control based problem, as well as proposing the method on learning optimal motion planning by means of a novel deep learning technique. We conduct extensive case studies to evaluate the performance of the proposed system model. Our experimental results demonstrate the effectiveness of the proposed system model under various traffic conditions. In addition, the robustness of the perception system is guaranteed by utilizing the Car Learning to Act (CARLA) system with available V2V communication.

Application of the Logistics Management System Based on Cloud Computing Technology

In order to solve the vehicle routing optimization problem of cold chain logistics distribution, this paper proposes a vehicle routing optimization method of cold chain logistics distribution based on cloud computing technology. This paper analyzes the advantages of processing the real-time routing of distribution vehicles under the cloud computing mode and establishes the application service architecture of vehicle routing optimization in cold chain logistics; under this architecture, multisource real-time traffic information is obtained, the vehicle distribution time and comprehensive cost are analyzed, the vehicle routing optimization model of cold chain logistics distribution is constructed, and the coarse-grained parallel genetic algorithm is used to solve the model in the cloud computing environment. The experimental results show that if W 1 = 0 , W 2 = 1 , the optimal path is 0-6-7-3-1-4-2-8-5, the total cost is 1620 yuan, the total mileage is 72 km, the vehicle driving time is 2.17 hours, and the calculation time is 10.79 s; let W 1 = 1 , W 2 = 0 , the optimal path is 0-5-6-7-2-4-3-1-8, the vehicle travel time is 1.84 hours, the total cost is 1736 yuan, the total mileage is 76 km, and the calculation time is 11.56 s. The optimal path is obtained in the effective time range. Conclusion. The real-time route optimization method of refrigerated vehicles in the cloud computing environment is effective. This method has practical significance to realize fine control of cold chain logistics distribution cost and improve distribution service efficiency.

Blockchain and deep learning based trust management for Internet of Vehicles

Internet of Vehicles (IoVs) works as the most advanced component of Intelligent Transportation Systems (ITSs). In IoVs, vehicles are able to communicate with nearby vehicles or RoadSide Units (RSUs). Traffic safety and efficiency can be provided by collecting and uploading real-time traffic information through vehicles, as well as broadcasting information by RSUs. However, there may be malicious vehicles in the network uploading false information, which will lead to serious traffic problems. To alleviate this problem, a trust management system based on blockchain technology is proposed in this paper. In this system, vehicles in the network firstly collect information about their surroundings and then upload valid information to nearby RSUs. To prevent malicious vehicles from uploading false messages, this paper designs a deep learning based verification model to calculate the trustworthiness of uploaded messages, and to further obtain the credibility scores of vehicles using the calculated results, and detect malicious vehicles accordingly. Moreover, a public blockchain framework is proposed and a Proof-Of-Trust (POT) consensus algorithm is designed. Vehicles are motivated to report true and valid information, and are penalized for uploading false data under this framework. Simulation results show that this mechanism can effectively detect malicious vehicles and motivate unfamiliar vehicles to upload true and reliable information to achieve trust management in the open and dynamic vehicular network environments.

Big Data Analytics for Mobility Prediction and Its Classification

Kern forecasting is the detection of which customers may leave a service or cancel a subscription for a service. This is an important forecast for many businesses because gaining new customers will cost more than retaining existing customers. The Retail Banking Seer Forecast is an AI-based model that helps customers assess the likelihood of your bank being blocked. Odor is a good indicator of growth. By comparing and analyzing these two metrics that monitor lost customers, and growth rates, new customers, Kern rates accurately tell you how much your business is growing over time. If growth is greater than recession, you can say that your business is growing. With increasing skill requirements and requirements for quality of experience, mobility forecasting has become widely used for mobile communication and has become one of the key processors that use historical transport information to predict the future locations of traffic users. Predictive maintenance refers to the use of data-driven, efficient maintenance methods, via designed to analyze the Status and maintenance of equipment When Predict what needs to be done. Forecasting maintenance is a type of maintenance that directly monitors the health, condition and performance of an asset in real time. Predictive maintenance is aimed at minimizing costly and unexpected breakdowns and gives the manufacturer the opportunity to plan maintenance around their own production schedule. Some examples of the use of forecast maintenance and forecast maintenance sensors include vibration analysis, oil analysis, Includes thermal imaging and equipment monitoring. This approach guarantees cost of savings in routine or time-based preventive maintenance, due to the tasks are only done when guaranteed. Predictive maintenance techniques to determine the condition of equipment in service Designed to help and evaluate when maintenance should be done. Big Data Analytics is structured, semi-structured, Is the use of advanced analytical techniques against very large, diverse datasets. and an unstructured data of various sizes ranging from different sources to terabytes to zeta bytes. Choosing a career in the field of big data and Analytics can be an exciting career endeavor, and it can be the type of role you are trying to find. Machine learning (ML) is a prediction that considers large-scale multidimensional data from a variety of sources allows you to create models. Several studies have been conducted on the use of ML algorithms to predict road traffic. Traffic forecast is floating car data and traffic flow, average traffic speed and based on historical traffic data such as traffic events The task is to predict real-time traffic information.

Complex attack detection scheme using history trajectory in internet of vehicles

The internet of vehicles technology provides convenience to drivers and prevents traffic accidents via wireless communication between road infrastructure and autonomous vehicles by sharing real-time traffic information. However, attackers can easily penetrate networks by exploiting the vulnerabilities of wireless communications. An attacker can falsify real-time traffic information and transmit it to a vehicle, causing traffic jams or preventing autonomous vehicles from receiving legitimate real-time traffic information. If autonomous vehicles do not receive accurate information, the arrival time at the destination can be affected, and accidents due to incorrect driving can occur. Because traffic accidents can cause casualties, they must be prevented. Various schemes have been proposed to detect attacks that occur on the internet of vehicles, and these security schemes can prevent traffic accidents by detecting attacks at high speeds. However, the existing schemes focus on quickly identifying a single attack but encounter difficulties when attempting to detect complex attacks that occur simultaneously. The proposed scheme uses a history trajectory to detect complex attacks. The proposed scheme stores behavioral information on all vehicles and road infrastructure using a control center. This information becomes a history trajectory that is used to detect attacks. Thereafter, when the vehicle is abnormally driven, the control center analyzes its driving path. When analyzing the vehicle driving process, the control center determines that an attack is being attempted when the road infrastructure or a vehicle makes an erroneous state transition. In addition, the type of attack is analyzed to identify compromised vehicles or road infrastructure and take measures to prevent further problems. Thus, the proposed scheme can detect complex attacks through history trajectory analysis. The experimental results demonstrate that in 80% of attempted attacks, the proposed scheme detects complex attacks with a probability of 97.56%.

Development and Testing of Road Signs Alert System Using a Smart Mobile Phone

Road traffic accident is a major problem worldwide resulting in significant morbidity and mortality. Advanced driver assistance systems are one of the salient features of intelligent systems in transportation. They improve vehicle safety by providing real-time traffic information to the driver. Road signs play an important role in road safety. To be effective, road signs must be visible at a distance that enables drivers to take the necessary actions. However, static road signs are often seen too late for a driver to respond accordingly. In this study, a system for alerting drivers about road signs has been developed and tested using a smart mobile phone. The study was carried out in Tanzania along an 80 km highway stretch from Arusha to Moshi town. The Haversine formula was used to measure and estimate the distance between two pairs of coordinates using the smartphone-based navigation application, Google Map. The application provides a voice alert to a needed action that enhances driver’s attention. We propose an alternative method that identifies and modifies a specific class of energy inefficiencies. According to the experimental results, the proposed methodology has the benefits of high accuracy within a user radius of 10 meters, minimum bandwidth, and low-cost application. Furthermore, the system application was secured by limiting access to the application program interface key to avoid unauthorized access to sensitive information.

Adaptive Speed Planning of Connected and Automated Vehicles Using Multi-Light Trained Deep Reinforcement Learning

Through shared real-time traffic information and perception of complex environments, connected and automated vehicles (CAVs) are endowed with global decision-making capabilities far beyond that of human drivers. Given more traffic lights information, this planning ability can be greatly strengthened. This study proposed an adaptive speed planning method of CAVs based on multi-light trained deep reinforcement learning (DRL), aiming to improve the fuel economy and comfort of CAVs. By setting a reasonable reward function, the training algorithm takes the key environmental information received by the vehicle as inputs, and finally outputs the acceleration that maximizes the cumulative reward. The results show that the trained DRL agent can adapt to variable scenarios with traffic lights, and quickly solve the approximate optimal speed trajectory. Multi-light DRL models save 6.79% fuel compared with single-light ones and have better performance on fuel economy and computational efficiency than a non-RL method using multi-light optimization.

Dynamic Multicompartment Refrigerated Vehicle Routing Problem with Multigraph Based on Real-Time Traffic Information

Aiming at the dynamic multicompartment refrigerated vehicle routing problem with multigraph based on real-time traffic information, this study, based on the idea of preoptimization followed by real-time adjustment, establishes a two-stage mathematical model with minimizing delivery cost. In the preoptimization phase, this study, based on historical traffic information, designed a hybrid chaotic genetic algorithm with variable neighborhood search (HCGAVNS) to obtain the initial delivery scheme. In the real-time adjustment phase, the order in which customers are served remains the same and a path selection strategy is proposed to solve the problem according to the real-time traffic information of different paths. The validity of the model and the algorithm are verified through the analysis of instances. The research results can enrich the related research on cold chain vehicle routing problem and provide a theoretical basis for logistics companies to optimize their delivery scheme.

ICDRP-F-SDVN: An innovative cluster-based dual-phase routing protocol using fog computing and software-defined vehicular network

Over time, wireless technology is evolving impulsively. For the most part, studies presently focused on the telecommunication field. Vehicular Ad-hoc Network (VANET) is one of the leading genuine and appealing areas for Intelligent Transportation Systems (ITS) originators and organizations. Essentially, VANET is an ad-hoc network consisting of several vehicles that flow on the same road. The existence of such networks revolutionizes the wireless communication field. The immense increase in the number of smart vehicles throughout time is the reason behind the massive evolution in VANET technologies. VANETs requirements are evolving quickly, therefore, having more flexible, scalable, and well-connected routes in VANETs is becoming essential in any new routing protocol. Antiquated VANETs routing protocols and designs encounter very challenging technical, employment, and control problems with the evolution of VANETs requirements. This paper presents a new efficient routing protocol for VANETs, namely, Innovative Cluster-Based Dual-Phase Routing Protocol Using Fog Computing and Software-Defined Vehicular Network (ICDRP-F-SDVN) which utilizes state-of-the-art technologies to overcome the drawbacks of traditional VANETs routing protocols. The use of fog computing and Software-Defined Networks (SDN) combined in our protocol presents a robust new architecture that meets all the new requirements and overcomes challenges arising from technological development and rapid escalation in the number of smart vehicles. Additionally, we propose a novel clustering algorithm mainly for the reason of lessening the long-distance communications in each cluster. In conjunction with this clustering algorithm, we propose an efficient control overhead reduction mechanism, thereby lowering the messages' exchanges imposed. Interestingly, our proposed protocol is a dual-phase protocol with a 90% of SDN packet delivery ratio. In other words, when the SDN fails to deliver packets, traditional Ad-hoc On-Demand Distance Vector (AODV) handles them as a fallback mechanism. In this paper, the performance of ICDRP-F-SDVN is evaluated in terms of throughput and end-to-end delay and further compared with four closely related routing protocols, that are, Intersection Dynamic VANET Routing (IDVR), Vehicle Density and Load Aware (VDLA), Intelligent Routing Protocol Using Real-Time Traffic Information in Urban Vehicular Environment (IRTIV), and Greedy Perimeter Coordinator Routing (GPCR). In terms of control overhead, the comparison is conducted with Control Overhead Reduction Algorithm (CORA), Moving Zone-Based Routing Protocol (MoZo), Beaconless Routing Algorithm for Vehicular Environment (BRAVE), and Cluster-Based Directional Routing Protocol (CBDRP). Our proposed protocol has achieved impressive simulation results compared to previously mentioned protocols. In particular, the throughput is increased by 8277%, 64309%, 76235%, and 96053% while the end-to-end delay is reduced by 77.5%, 82.5%, 87.2%, 95.6% over IDVR, VDLA, IRTIV, and GPCR, respectively. Finally, the control overhead is lowered by 42.3%, 87%, 93.5%, 96.7% over that of CORA, MoZo, BRAVE, and CBDRP, respectively.

Routing Optimization of Electric Vehicles for Charging With Event-Driven Pricing Strategy

With the increasing market penetration of electric vehicles (EVs), the charging behavior and driving characteristics of EVs have an increasing impact on the operation of power grids and traffic networks. Existing research on EV routing planning and charging navigation strategies mainly focuses on vehicle-road-network interactions, but the vehicle-to-vehicle interaction has rarely been considered, particularly in studying simultaneous charging requests. To investigate the interaction of multiple vehicles in routing planning and charging, a routing optimization of EVs for charging with an event-driven pricing strategy is proposed. The urban area of a city is taken as a case for numerical simulation, which demonstrates that the proposed strategy can not only alleviate the long-time queuing for EV fast charging but also improve the utilization rate of charging infrastructures. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was inspired by the concerns of difficulties for electric vehicle (EV)’s fast charging and the imbalance of the utilization rate of charging facilities. Existing route optimization and charging navigation research are mainly applicable to static traffic networks, which cannot dynamically adjust driving routes and charging strategies with real-time traffic information. Besides, the mutual impact between vehicles is rarely considered in these works in routing planning. To resolve the shortcomings of existing models, a receding-horizon-based strategy that can be applied to dynamic traffic networks is proposed. In this article, various factors that the user is concerned about within the course of driving are converted into driving costs, through which each road section of traffic networks is assigned the corresponding values. Combined with the graph theory analysis method, the mathematical form of the dynamic traffic network is presented. Then, the article carefully plans and adjusts EV driving routes and charging strategies. Numerical results demonstrate that the proposed method can significantly increase the adoption of EV fast charging while alleviating unreasonable distributions of regional charging demand.

FECO: An Efficient Deep Reinforcement Learning-Based Fuel-Economic Traffic Signal Control Scheme

Vehicle fuel efficiency (VFE) has a pivotal role in solving energy shortage issue due to the increasing global demand for energy. The high frequency of go-stop movements and long waiting times at intersections significantly reduce the VFE. Such negative impacts are particularly severe when the traffic flows are regulated by poorly designed traffic signal control. Existing works have successfully applied deep reinforcement learning (DRL) techniques to improve the efficiency of traffic signal control. However, to the best of our knowledge, few studies have explored traffic signal control for VFE through eco-driving techniques. To fill the gap, we propose a DRL-based fuel-economic traffic signal control for improving vehicle fuel efficiency. Briefly, we adopt the DRL-technique to develop an agent that can efficiently control traffic signals based on real-time traffic information at intersections, and adjust speed profiles for approaching vehicles to smooth traffic flows. We tested our method on both synthetic traffic dataset and real-world traffic dataset from surveillance cameras in Toronto. Through comprehensive experiments, we demonstrate that our method surpassed the performance of both pure eco-driving and pure traffic signal control techniques by significantly reducing vehicle fuel consumption and improving the efficiency of traffic signal control.

Communication platform concept for virtual testing of novel applications for railway traffic management systems

In recent years, the railway system has been facing the various challenges of the “digital age”. To increase its attractiveness, capacity, sustainability, and security, it needs to improve its’ everyday operational and planning process. This can be enabled using new generation digitised and automated Traffic Management Systems (TMS). Nowadays, railway dispatchers need a TMS that offers precise and real-time traffic information as fundamental condition for effective traffic management, and whose performance can be further improved by increasing availability and diversity of sources and data, for which an effective data management platform is required. The EU-funded OPTIMA project is designing and developing a communication platform to manage the connection between several services supporting TMS applications, also enabling their testing. It represents one of the steps required for the development and implementation of a new generation of TMS. This paper describes the concept for the OPTIMA platform which will link TMS applications used by railway dispatchers with infrastructure systems such as signalling and interlocking systems, maintenance, and energy management, as well as with data services (such a weather information) with the aim of providing standardised interfaces and common data structures as basis of a common and standardised communication.

On-Road Safety Prompt Framework for Platoon

In recent years, researchers are very interested in the development of autonomous vehicle platoons that can be commercialized on a large scale. The research progress in this field raises the new demand for real-time traffic flow information and safety prediction. Therefore, an On-road Safety Prompt Framework (OSPF) that combines the free space detection and lane change prediction has beed developed by this paper. This OSPF can predict the lane-change decision and lane-change trajectory around in a short period of time in the future according to the current state and historical trajectory of the surrounding vehicles, and combine these prediction information to provide the autonomous vehicle platoon with the predicted free space in the near future. The free space provided by this OSPF includes three types: continuing driving according to the current state or changing lanes to the left or right. At the same time, it also provides the required recommended driving trajectory. The evaluation test of this ADAS has been done through a simulation testbed of the NGSIM dataset built with Eclipse SUMO (Lopez et al. (2018)).

Security of communication 5G-V2X: A proposed approach based on securing 5G-V2X based on Blockchain

With the rapid evolution of wireless communication and autonomous vehicles, vehicle to Everything (V2X) communications provides driving safety, traffic efficiency, and real-time traffic information in-vehicle networks. V2X has evolved by integrating 5G cellular access technology and New Radio (NR) into V2X communications (i.e., 5G NR V2X). Since the security issue in 5G-V2X remains a crucial point, we conducted a literature review approach and identified the 5G-V2X communication levels in this paper. The main objective of this study was to develop an approach to secure 5G-V2X. The proposed methodology is based on Blockchain to ensure the 5G-V2X architecture levels.

A systematic approach for microscopic models based on cellular automata for road traffic

The transportation infrastructure is one of the most important resources for a country's economic and social well-being. The effectiveness of a country's street network will decide whether it develops further or stagnates. With the increasing number of vehicles on the road and the effects of urbanization, traffic roads are being subjected to a variety of requests and uses for which they were not designed, sized, or predicted. Because of the critical relevance of traffic roads, research must begin to lessen the effects of traffic jams in the streets, determine the appropriate number of traffic lanes, and integrate real-time traffic information into GPS systems. The goal of modeling a traffic-road system is to either build new traffic systems or gain a better knowledge of existing traffic systems so that they can be optimized. The accuracy, performance, stochastic and dynamic behavior of the model produced will be evaluated using a simulation of a genuine traffic system. This paper provides microscopic models based on cellular automation to replicate the behavior of various automobiles on a set of urban streets in Cluj Napoca city downtown. This model includes streets with multiple traffic lanes, various types of vehicles such as automobiles, buses, and trams, intersections with multiple possible upcoming streets controlled by traffic lights, bus stops inside and outside the traffic lane, tram stops inside the traffic lane, pedestrian crosswalks, and parking areas alongside and transversely with the right traffic lane of a street. TCA (Traffic Cellular Automata) is a proposed model that produces adequate findings in urban traffic theory. The results were obtained in both free-flow and traffic-jam conditions.

Delay Factors Modelling for Real-Time Traffic Information Systems

The paper is presenting an overview of the factors that lead to critical delays which may occur in multi-modal realtime traffic and travel information systems (MRTTI) employing cellular data network communications. The analysis is taking into account MRTTI systems that use mobile devices to detect position and speed of a vehicle and receive traffic information furthermore used to adjust a previously defined route from origin to destination points. Information transmission delays are also investigated for route guidance systems used in critical applications, such as emergency vehicles management. The identification of the delay factors and some models regarding the delays are presented along with measurements on a real system.

A low-power and low cost smart streetlight system based on Internet of Things technology

Street lighting, as the most essential and universal component of the urban lighting system, accounts for a large portion of public electricity usage. Therefore, improving street lamps working efficiency is vital for energy savings. This paper demonstrates the design of a smart street lighting system supported by the combination of NB-IoT and LoRa communication technology. By adopting an optimized street lamp control algorithm, the system can realize the automatic control of street lights according to the real-time traffic flow information. This system has been installed on Luyang Avenue, Lucheng City, Shanxi Province, China in May 2019. It managed to reduce the electricity consumption in this region significantly in June 2019, which was 18% lower than that in April 2019 and 19.7% lower than that in June 2018. By illustrating the unique advantages of this system in energy savings and cost reduction, this paper displays its potential for further application in the construction of smart cities on a large scale.

Reliability of variable message signs information: A field experiment study in Chongqing

A variable message sign (VMS), which is a key component of intelligent transportation systems, has been frequently used in the management of urban roads and motorways to provide drivers with real-time traffic condition information about a road section or area. Nevertheless, there is a lack of unified regulations for VMS design, which makes it difficult to accurately determine the understandability and legibility of VMS information. In practice, inappropriate designs of VMSs are common, such as overload of VMS information and excessive number of phases, particularly on the urban roads of China. Building on our earlier findings obtained by surveys, in this study, field tests were conducted to assess the reliability of VMS information in terms of the visual perception characteristics of drivers. For a full visual perception, the information displayed on a VMS must be reliable, i.e., it should be easy for drivers to detect, obtain, and understand the messages they require. Two basic theories—information theory and visual perception theory—were introduced to build a quantification model of VMS information, select evaluation indices, and design the experimental process. A total of 24 drivers participated in the field experiments and questionnaires. Five indices—information obtainment rate, periodic validity, driver subjective scoring and the corresponding UI (represents information intelligibility), and CE (represents information legibility)—were analyzed to evaluate their relationships to VMS information reliability. The results confirmed that the amount of information, and the scrolling period considering redundant distance, significantly affect the reliability of VMSs information. For static VMSs, the information obtainment rate and the subjective scoring decrease with increasing amount of information. The recollection accuracy of the drivers significantly declines when the amount of information shown increases to 90 bits, corresponding to an information obtainment rate of less than 0.8 and UI equal to 0. For dynamic VMSs, the information reliability deteriorates as the scrolling period shortens and the number of phases increases. Unreasonable reliability is found when the periodic validity is less than 0.9, i.e., the actual scrolling period is more than 10% less than the calculated one, corresponding to CE equal to 0. The reliability of information was evaluated by combining the subjective scoring of the drivers and a data-based statistical analysis and considering driving safety. Accordingly, it was recommended that 90 bit is the maximum amount of information to be shown on a VMS and that the preferred scrolling period of each phase of a dynamic VMS is 5 s. The results of this study support the objective of providing reliable information to drivers by addressing the problems related to the amount of information and presentation time for VMSs. These findings provide a basis for determining the thresholds for VMS information to promote practical and user-friendly designs of VMSs on urban roads.