Regulation has shaped the evolution of the automobile from the beginning. In Moving Violations, Lee Vinsel shows that, contrary to popular opinion, these restrictions have not hindered technological ...change. Rather, by drawing together communities of scientific and technical experts, auto regulations have actually fostered innovation.
Vinsel tracks the history of American auto regulation from the era of horseless carriages and the first, faltering efforts to establish speed limits in cities to recent experiments with self-driving cars. He examines how the government has tried to address car-related problems, from accidents to air pollution, and demonstrates that automotive safety, emissions, and fuel economy have all improved massively over time. Touching on fuel economy standards, the rise of traffic laws, the birth of drivers' education classes, and the science of distraction, he also describes how the government's changing activities have reshaped the automobile and its drivers, as well as the country's entire system of roadways and supporting technologies, including traffic lights and gas pumps.
Moving Violations examines how policymakers, elected officials, consumer advocates, environmentalists, and other interested parties wrestled to control the negative aspects of American car culture while attempting to preserve what they saw as its positive contributions to society. Written in a clear, approachable, and jargon-free voice, Moving Violations will appeal to makers and analysts of policy, historians of science, technology, business, and the environment, and any readers interested in the history of cars and government.
Traffic congestion has become a vexing and complex issue in many urban areas. Of particular interest are the intersections where traffic bottlenecks are known to occur despite being traditionally ...signalized. Reinforcement learning (RL), which is an artificial intelligence approach, has been adopted in traffic signal control for monitoring and ameliorating traffic congestion. RL enables autonomous decision makers (e.g., traffic signal controllers) to observe, learn, and select the optimal action (e.g., determining the appropriate traffic phase and its timing) to manage traffic such that system performance is improved. This article reviews various RL models and algorithms applied to traffic signal control in the aspects of the representations of the RL model (i.e., state, action, and reward), performance measures, and complexity to establish a foundation for further investigation in this research field. Open issues are presented toward the end of this article to discover new research areas with the objective to spark new interest in this research field.
Traffic flow forecasting is a necessary part in the intelligent transportation systems in supporting dynamic and proactive traffic control and making traffic management plan. However, most of the ...previous studies attempting to build traffic flow forecasting models focus on short-term forecasting as the next step. In this paper, a deep feature leaning approach is proposed to predict short-term traffic flow in the following multiple steps using supervised learning techniques. To achieve traffic flow forecasting for the next day, an advanced multi-objective particle swarm optimization algorithm is applied to optimize some parameters in deep belief networks. The modified model can boost the accuracy of the forecasting results and enhance its multiple step prediction ability. Using real-time and historical temporal–spatial traffic data, day-ahead prediction experiment is implemented. The results of the hybrid model are compared with several commonly used benchmark models and some improved deep neural network based on evaluation criteria. Also, the proposed optimization algorithm is compared with the traditional particle swarm optimization algorithm. Furthermore, the significance in the number of hidden layers is analyzed. When the layers are increasing more than 4, the performance of the proposed model stops improving significantly. The results indicate the proposed model can extract complex features of traffic flow and therefore the forecasting accuracy and stability can be effectively improved.
In smart cities, the fast increase in automobiles has caused congestion, pollution, and disruptions in the transportation of commodities. Each year, there are more fatalities and cases of permanent ...impairment due to everyday road accidents. To control traffic congestion, provide secure data transmission also detecting accidents the IoT-based Traffic Management System is used. To identify, gather, and send data, autonomous cars, and intelligent gadgets are equipped with an IoT-based ITM system with a group of sensors. The transport system is being improved
machine learning. In this work, an Adaptive Traffic Management system (ATM) with an accident alert sound system (AALS) is used for managing traffic congestion and detecting the accident. For secure traffic data transmission Secure Early Traffic-Related EveNt Detection (SEE-TREND) is used. The design makes use of several scenarios to address every potential problem with the transportation system. The suggested ATM model continuously modifies the timing of traffic signals based on the volume of traffic and anticipated movements from neighboring junctions. By progressively allowing cars to pass green lights, it considerably reduces traveling time. It also relieves traffic congestion by creating a seamless transition. The results of the trial show that the suggested ATM system fared noticeably better than the traditional traffic-management method and will be a leader in transportation planning for smart-city-based transportation systems. The suggested ATM-ALTREND solution provides secure traffic data transmission that decreases traffic jams and vehicle wait times, lowers accident rates, and enhances the entire travel experience.
Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure ...time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short-term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short-term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal–spatial correlation in traffic system via a two-dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance.
•A universal behavior mechanism is revealed for human-driven traffic.•The fundamental diagrams of car, bicycle and pedestrian traffic can be unified through a single scaled speed-spacing equation.•A ...universal social force model is constructed to reproduce the experimental results.
In this research we performed new bicycle and pedestrian experiments to supplement data extracted from existing follow-the-leader experiments in vehicles, bicycles and pedestrians, and studied their spacetime trajectories and flow-density (or spacing-velocity) phase diagrams. The strong similarities in the spacetime trajectories and the bi-variate phase plots as well as the relative consistence of the estimated proportionality parameter across all three types of traffic, suggest that a unified behavioral mechanism is at play in human-driven traffic. It is suggested that this mechanism is essentially a safety-driven behavior that vehicles, bicycles or pedestrians adopt a safe speed for a given spacing between them. This behavior is well described by a well-known model in vehicular traffic and it is shown in this paper that a scaled version of this model applies to all three types of traffic. A unified relaxation-driven social force traffic model is then proposed to incorporate this behavior mechanism. Simulations with the same setup as the real-life experiments were carried out for vehicle, bicycle, and pedestrian traffic using the unified traffic model and the simulated spacetime trajectories and fundamental diagrams show remarkable consistence with the experimental results.
The present era is marked by rapid improvement and advances in technology. One of the most essential areas that demand improvement is the traffic signal, as it constitutes the core of the traffic ...system. This demand becomes stringent with the development of Smart Cities. Unfortunately, road traffic is currently controlled by very old traffic signals (tri-color signals) regardless of the relentless effort devoted to developing and improving the traffic flow. These traditional traffic signals have many problems including inefficient time management in road intersections; they are not immune to some environmental conditions, like rain; and they have no means of giving priority to emergency vehicles. New technologies like Vehicular Ad-hoc Networks (VANET) and Internet of Vehicles (IoV) enable vehicles to communicate with those nearby and with a dedicated infrastructure wirelessly. In this paper, we propose a new traffic management system based on the existing VANET and IoV that is suitable for future traffic systems and Smart Cities. In this paper, we present the architecture of our proposed Intelligent Traffic Management System (ITMS) and Smart Traffic Signal (STS) controller. We present local traffic management of an intersection based on the demands of future Smart Cities for fairness, reducing commute time, providing reasonable traffic flow, reducing traffic congestion, and giving priority to emergency vehicles. Simulation results showed that the proposed system outperforms the traditional management system and could be a candidate for the traffic management system in future Smart Cities. Our proposed adaptive algorithm not only significantly reduces the average waiting time (delay) but also increases the number of serviced vehicles. Besides, we present the implemented hardware prototype for STS.
•Fundamental diagram over a wide range of possible traffic states.•S-shaped three-parameter (S3) traffic flow model.•Microscopic car-following model consistent with S3 model.•Calibration with ...real-world traffic data.
In this study, a new s-shaped three-parameter (S3) traffic flow model is proposed to represent the relationships between three fundamental variables (i.e., flow, speed, and density) in highway traffic. An s-shaped speed-density function is proposed to capture the speed-density relationship under a wide range of possible densities. A consistent car-following model was derived in terms of the proposed s-shaped speed-density function. Both the S3 macroscopic model and the derived microscopic car-following model were calibrated using real-world traffic data.
Autonomous vehicles are being viewed with scepticism in their ability to improve safety and the driving experience. A critical issue with automated driving at this stage of its development is that it ...is not yet reliable and safe. When automated driving fails, or is limited, the autonomous mode disengages and the drivers are expected to resume manual driving. For this transition to occur safely, it is imperative that drivers react in an appropriate and timely manner. Recent data released from the California trials provide compelling insights into the current factors influencing disengagements of autonomous mode. Here we show that the number of accidents observed has a significantly high correlation with the autonomous miles travelled. The reaction times to take control of the vehicle in the event of a disengagement was found to have a stable distribution across different companies at 0.83 seconds on average. However, there were differences observed in reaction times based on the type of disengagements, type of roadway and autonomous miles travelled. Lack of trust caused by the exposure to automated disengagements was found to increase the likelihood to take control of the vehicle manually. Further, with increased vehicle miles travelled the reaction times were found to increase, which suggests an increased level of trust with more vehicle miles travelled. We believe that this research would provide insurers, planners, traffic management officials and engineers fundamental insights into trust and reaction times that would help them design and engineer their systems.
Traffic lights have been installed throughout road networks to control competing traffic flows at road intersections. These traffic lights are primarily intended to enhance vehicle safety while ...crossing road intersections, by scheduling conflicting traffic flows. However, traffic lights decrease vehicles’ efficiency over road networks. This reduction occurs because vehicles must wait for the green phase of the traffic light to pass through the intersection. The reduction in traffic efficiency becomes more severe in the presence of emergency vehicles. Emergency vehicles always take priority over all other vehicles when proceeding through any signalized road intersection, even during the red phase of the traffic light. Inexperienced or careless drivers may cause an accident if they take inappropriate action during these scenarios. In this paper, we aim to design a dynamic and efficient traffic light scheduling algorithm that adjusts the best green phase time of each traffic flow, based on the real-time traffic distribution around the signalized road intersection. This proposed algorithm has also considered the presence of emergency vehicles, allowing them to pass through the signalized intersection as soon as possible. The phases of each traffic light are set to allow any emergency vehicle approaching the signalized intersection to pass smoothly. Furthermore, scenarios in which multiple emergency vehicles approach the signalized intersection have been investigated to select the most efficient and suitable schedule. Finally, an extensive set of experiments have been utilized to evaluate the performance of the proposed algorithm.
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