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Ensuring the correctness of road network data is critical for navigation, traffic control and urban planning. Errors like missing roads and absent connections can hinder its quality. Trajectory data emerges as a cost-effective source to uncover such errors. Existing methods often analyze the mismatches between trajectories and road networks to identify specific errors. They heavily rely on manually established rules and fail to fully leverage the diverse patterns of trajectories and the underlying road network structure. The article introduces a sequential classification approach to detect diverse road network errors. It starts with partial map matching (PMM) to associate trajectories with a road network, allowing unmatched portions. Context features are subsequently extracted by encoding patterns in the map matching (MM) outputs, raw trajectories and road network. Finally, a bidirectional recurrent neural network (BiRNN) model is trained to identify the network error category for each trajectory point. Experiments were performed on detecting errors in OpenStreetMap (OSM) road network with a real-world trajectory dataset. It demonstrates that the proposed method achieves accuracy over 96%, significantly surpassing four baselines. An ablation study confirms the necessity of considering different types of context features. This method advances error detection by effectively utilizing trajectories in identifying diverse network errors. [ABSTRACT FROM AUTHOR]

This manuscript presents an Adam optimization-based Deep Reinforcement Learning model for Mixed Traffic Flow control (ADRL-MTF), so as to guide Connected and Autonomous vehicle's (CAV) longitudinal trajectory on a typical urban roadway with signal-controlled intersections. Two improvements are made when compared with prior literatures. First, the common assumptions to simplify the problem solving, such as dividing a vehicle trajectory into several segments with constant acceleration/deceleration, are avoided, to improve the modeling realism. Second, built on the efficient Adam Optimization and Deep Q-Learning, the proposed model avoids the enumeration of states and actions, and is computational efficient and suitable for real time applications. The mixed traffic flow dynamic is firstly formulated as a finite Markov decision process (MDP) model. Due to the discretization of time, space and speed, this MDP model becomes high-dimensional in state, and is very challenging to solve. We then propose a temporal difference-based deep reinforcement learning approach, with ε -greedy for exploration-exploitation balance. Two neural networks are developed to replace the traditional Q function and generate the targets in the Q-learning update. These two neural networks are trained by the Adam optimization algorithm, which extends stochastic gradient descent and considers the second moments of the gradients, and is thus highly computational efficient and has lower memory requirements. The proposed model is shown to reduce fuel consumption by 7.8%, which outperforms a prior benchmark model based on Monte Carlo Tree Search. The model's runtime efficiency and stability are tested, and the sensitivity analysis is also performed. [ABSTRACT FROM AUTHOR]

Intelligent traffic control and guidance system is an effective way to solve urban traffic congestion, improve road capacity and guarantee drivers' travel safety, while short-term traffic flow prediction is the core of intelligent traffic control and guidance system. To investigate the long-term memory and the dynamic feature of short-time traffic flow time series, a hybrid model was proposed by integrating autoregressive fractionally integrated moving average (ARFIMA) model and nonlinear autoregressive (NAR) neural network model to predict short-time traffic flow, in which ARFIMA model can address the long-term memory of linear component and NAR neural network can accommodate the dynamic feature of nonlinear residual component. First, the ARFIMA model was employed to predict the linear component of traffic flow, and the results were compared with those of autoregressive integrated moving average (ARIMA) model. Next, the NAR neural network model was adopted to forecast the nonlinear residual components, and the weighted results were considered as the predicted flow of the hybrid model. The proposed hybrid model was validated by using the cross-sectional traffic flow data in California freeways obtained from the open-access PeMS database. The results showed that the ARFIMA model considering the long-term memory can effectively predict the short-term traffic flow, and the prediction accuracy of the hybrid model is better than that of the singular models. The findings provide an alternative for the short-term traffic flow prediction with lower error and higher accuracy. [ABSTRACT FROM AUTHOR]

Concerning the critical role of Intelligent Transportation System (ITS) in tackling the problem of traffic congestion, and enhancing the safety, mobility and sustainability of the transport network, this paper provides a comprehensive review of recent developments in Connected Vehicle (CV) technology, its features and terminology. The real-world implementation of the CV technologies can result in a better understanding of the practical and technical issues. Given the technical and conceptual challenges, as well as novelty factors associated with CVs, many CV pilot studies around the world are reviewed, and their findings are summarized. The paper contributes to the literature by reviewing the characteristics, communication platform, multimodality, size and type of study areas of the pilot CV testbeds across the world. This research also paves the way for the better development of CV technology by comparing and discussing the pros and cons of the existing CV pilot testbeds. Moreover, we have discussed the evaluation plans and the advantages and disadvantages of different testbeds. Besides, as a main component of the CV system, the traffic signal control algorithms, as well as transit signal priority in a CV ecosystem, are also reviewed. Based on our findings, the real-time data obtained from the CV system could result in a better adjustment of traffic signal parameters which consequently enhance the traffic regulation in the network (say up to 30% in terms of travel time in comparison with an actuated coordinated traffic signal). However, a major criticism of the existing traffic signal strategies in a CV system is that they do not utilize the maximum capability of the CV system (V2 V, V2I and I2I communications). [ABSTRACT FROM AUTHOR]

The width of street rights-of-way is normally determined by traffic engineering and urban design conventions, without considering the immense value of the underlying land. In this article, I develop an economic framework that can inform decisions on street width, and I use tax parcel data to quantify the widths, land areas, and land value of streets in 20 of the largest counties in the United States. Residential street rights-of-way in the urbanized portion of these counties average 55 ft wide, far greater than the functional minimum of 16 ft required for access. The land value of residential streets totals $959 billion in the urbanized portion of the 20-county sample. In most counties, subdivision regulations are binding. That is, few developers choose to build streets that are wider than code requirements, implying that softening requirements would mean more land devoted to housing and less to streets. Although I highlight the potential for narrower street rights-of-way, I did not consider detailed design issues. Nor did I analyze how any windfall from reduced land requirements would be divided among landowners, developers, and house purchasers. Particularly in places with high land values and housing costs, reallocating street rights-of-way to housing would increase economic efficiency. In the most expensive county in the data set—Santa Clara (CA)—narrowing the right-of-way to 16 ft would save more than $100,000 per housing unit through reduced land consumption. Where streets have little or no function for through traffic, the costs and benefits accrue almost exclusively to neighborhood residents. Thus, planners could reduce or even eliminate street width requirements in subdivision ordinances, leaving developers to make the trade-off between land for streets and land for housing. [ABSTRACT FROM AUTHOR]

A signalized intersection regulated by pre-timed control in an urban traffic network is considered. A method for computing the mean delay of vehicles based on the theory of queues with server vacations is adapted from the existing literature and an algorithm for numerical implementation is proposed. Arrivals are assumed to occur according to a batch Markovian arrival process (BMAP). The proposed algorithm is an alternative to the previous methods that includes a numerical improvement in the computation of the queue invariant probabilities. In the case of isolated intersections we compare the new algorithm with other traffic models, including simulation model, Webster model and the Highway Capacity Manual model. The mean delay of vehicles given by the new algorithm is a short distance away from the mean delay obtained in the simulations. The approximation for large flows given by the proposed algorithm is as good as the Highway Capacity Manual model and with no need to tune unknown parameters. Our results are of interest for traffic engineers, since other approaches have been subject to criticism when flows near capacity are involved. [ABSTRACT FROM AUTHOR]

Traffic congestion induces significant economic loss each year, and identifying traffic congestion patterns is necessary for better traffic control and management. Floating car data (FCD) provides a cost-effective alternative for assessing traffic status and detecting congestion on a large scale. Recently, a new speed performance index (SPI) has been proposed to evaluate traffic status considering both traffic flow speeds and road speed limits. This research proposes a new categorization criterion to define traffic conditions as five levels based on SPI values, and applies the proposed criterion in a case study to investigate traffic status and detect traffic congestion patterns on urban freeways based on FCD analysis. The research results reveal a clear understanding regarding urban freeway traffic status and congestion spatial and temporal distribution. With more comprehensive FCD from larger spatial and temporal domains, continued research could focus on travel speed prediction, travel time estimation, prediction, and reliability analysis at both the road segment and network levels. [ABSTRACT FROM AUTHOR]

Timing urban traffic is moral work. In this paper, I show how the moral work that goes into timing traffic lights addresses both industrial, civic, and domestic worth in ways that cut generalization short. Relying upon ethnographic fieldwork, this paper focuses on how municipal traffic engineering maintains traffic lights and handles complaints about them. Steeped in moral ambiguity, the paper argues, municipal traffic engineering resorts to singularizing complaints, truncating worth, and working with careful dedication from one contestable compromise to the next. With this argument, the paper contributes to an understanding of the temporal and moral orderings at stake in adjusting and justifying urban mobilities. [ABSTRACT FROM AUTHOR]

Urban commuters have been suffering from traffic congestion for a long time. In order to avoid or mitigate the congestion effect, it is significant to know how the introduction of autonomous vehicles (AVs) influence the road capacity. The effects that AVs bring to the macroscopic fundamental diagram (MFD) were investigated through microscopic traffic simulations. This is a key issue as the MFD is a basic model to describe road capacity in practical traffic engineering. Accordingly, the paper investigates how the different percentage of AVs affects the urban MFD. A detailed simulation study was carried out by using SUMO both with an artificial grid road network and a real-world network in Budapest. On the one hand, simulations clearly show the capacity improvement along with AVs penetration growth. On the other hand, the paper introduces an efficient modeling for MFDs with different AVs rates by using the generalized additive model (GAM). [ABSTRACT FROM AUTHOR]

Clarifying the spatiotemporal characteristics of urban traffic congestion can help to manage and alleviate such congestion more effectively. In this study, we used real-time traffic data provided by the Gaode LBS open platform to model and analyze urban traffic congestion in Xi'an, China, focusing on the spatiotemporal characteristics of single roads, different road grades, and the whole road network at different moments and time periods. Weekday real-time traffic data were collected from 23 April to 11 May, 2018, and the congestion index for different types of roads within Xi'an's Third Ring Road was calculated. The results allowed us to thoroughly analyze the spatiotemporal characteristics of traffic congestion in Xi'an and suggest methods for congestion control and improvement, including enhanced traffic control during special periods and more balanced urban development. Our results show the feasibility of this evaluation approach, which can provide timely decision-making support for urban traffic congestion management. [ABSTRACT FROM AUTHOR]

This evaluation ascertained the operational impacts of the SUTRAC (Scalable Urban Traffic Control) Adaptive Signal Control Technology (ASCT) in an urban corridor consisting of 23 intersections in Pittsburgh, Pennsylvania. A combination of real-world GPS floating car runs and private sector probe data from INRIX was used to assess the impact of the ASCT. Data were collected with the ASCT active and inactive to determine the operational impacts on the mainline and cross streets. The ASCT was found to produce significant improvements in the number of stops made along the corridor. On Baum and Center, travel times improved during the AM and PM peak in the WB direction. Speeds were also observed to improve significantly during the Midday period on Baum EB and during the AM and PM peak periods on Baum WB. Similarly, statistically significant improvements in speed were observed on Center WB during the AM and PM periods, while a statistically significant decrease in speed was observed during the Midday period. Six months of private sector probe data was used to examine travel time reliability along the corridor, and reliability was also found to have improved. Further, Bayesian models were calibrated to account for variations in speeds and acceleration/deceleration. The Bayesian models revealed that driving was less volatile with the ASCT system in operation over instantaneous periods, which also points towards improved operations. The findings of this study are generally consistent with past evaluations of other ASCTs, indicating that the SURTRAC system is another potential tool for managing congestion on signalized urban arterial networks. [ABSTRACT FROM AUTHOR]

Vancouver has no freeways and is considered highly walkable. To move traffic on streets but retain pedestrian friendliness, the city uses pedestrian activated half signals where minor streets intersect arterials, involving signal lights for the arterials and stop signs for the minor streets. Having both controls at an intersection is discouraged by widely adopted traffic engineering standards because of the complexity it creates. Inspired by 'safety in complexity' literature, this paper presents research exploring how these 'multi-controlled' intersections are deployed and work in practice, and their safety records. Findings suggest they are not unsafe and users adapt to their complexity. [ABSTRACT FROM AUTHOR]

Traffic congestion is a critical problem which makes roads busy. Traffic congestion challenges traffic flow in urban areas. A growing urban area creates complex traffic problems in daily life. Congestion phenomena cannot be resolved only by applying physical constructs such as building bridges and motorways and increasing road capacity. It is necessary to build technological systems for transportation management to control the traffic phenomenon. In this article, a new idea is proposed to tackle traffic congestion with the aid of machine learning approaches. A new strategy based on a tree-like configuration (i.e. a decision-making model) is suggested to handle traffic congestion at intersections using adaptive traffic signals. Different traffic networks with different sizes, varying from nine to 400 intersections, are examined. Numerical results and discussion are presented to prove the efficiency and application of the proposed strategy to alleviate traffic congestion. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization framework for urban transportation networks' (re-)design which explicitly takes into account the specific decision-making processes of ordinary users and logistic operators. Ordinary users are typically commuters whose travels consist of well-defined pairs of origin and destination points, while logistic operators make deliveries at multiple locations. Obviously, these two user classes have different objectives and scopes of action. These differences are seldom considered in traffic research since most models aggregate the flow demand in OD matrices and use assignment models to predict the response of all users as if the dynamics of their optimization processes were of the same nature. This work demonstrates that better results can be achieved if the particular features of each user class are included in the models. It potentially improves the estimation of the responses and allows managers to shape their control measures to address specific user needs. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study of centralised and decentralised architectures for managing urban road network efficiency with consideration of users' responses and uncertainties with respect to prevailing traffic conditions. The control systems are applied to different network topologies with different levels and spatial distributions of traffic demand. The study reveals that the computationally effective decentralised systems could perform almost as well as the conventional centralised one when users' responses are taken into account with provision of real-time traffic information. This generates new insight on cooperative transport management with applications of information and communication technology. [ABSTRACT FROM AUTHOR]

Existing performance models developed for interurban pavements are not applicable to urban pavements due to differences in traffic demands and deterioration trends. The objective of the study was to develop performance models for the management of urban pavement networks. Markov chains and Monte Carlo simulation were applied to account for the probabilistic nature of pavements deterioration over time, using data collected in the field. One of the advantages of this methodology is that it can be used by local agencies with scarce technical resources and historical data. Eight performance models were developed and successfully validated for asphalt and concrete pavements in humid, dry and Mediterranean climates with different functional hierarchies. The resulting models evidence the impact of design, traffic demand, climate and construction standards on urban pavements performance. Predicted service life of asphalt and concrete pavements in primary networks are consistent with design standards. However, pavements in secondary and local networks present shorter and longer service life compared to design life, respectively. Climate is a relevant factor for asphalt pavements, where higher deterioration was observed compared to that expected. Opposite to this, no relevant differences between design and performance can be attributed to climate in concrete pavements. [ABSTRACT FROM AUTHOR]

Short-term traffic prediction plays an important role in intelligent transport systems. This paper presents a novel two-stage prediction structure using the technique of Singular Spectrum Analysis (SSA) as a data smoothing stage to improve the prediction accuracy. Moreover, a novel prediction method named Grey System Model (GM) is introduced to reduce the dependency on method training and parameter optimisation. To demonstrate the effects of these improvements, this paper compares the prediction accuracies of SSA and non-SSA model structures using both a GM and a more conventional Seasonal Auto-Regressive Integrated Moving Average (SARIMA) prediction model. These methods were calibrated and evaluated using traffic flow data from a corridor in Central London under both normal and incident traffic conditions. The prediction accuracy comparisons show that the SSA method as a data smoothing step before the application of machine learning or statistical prediction methods can improve the final traffic prediction accuracy. In addition, the results indicate that the relatively novel GM method outperforms SARIMA under both normal and incident traffic conditions on urban roads. [ABSTRACT FROM PUBLISHER]

The history of urban traffic control (UTC) throughout the past century has been a continued race to keep pace with ever more complex policy objectives and consistently increasing vehicle demand. Many benefits can be observed from an efficient UTC system, such as reduced congestion, increased economic efficiency and improved road safety and air quality. There have been significant advances in vehicle detection and communication technologies which have enabled a series of step changes in the capabilities of UTC systems, from early (fixed time) signal plans to modern integrated systems. A variety of UTC systems have been implemented throughout the world, each with individual strengths and weaknesses; this paper seeks to compare the leading commercial systems (and some less well known systems) to highlight the key characteristics and differences before assessing whether the current UTC systems are capable of meeting modern transport policy obligations and desires. This paper then moves on to consider current and future transport policy and the technological landscape in which UTC will need to operate over the coming decades, where technological advancements are expected to move UTC from an era of limited data availability to an era of data abundance. [ABSTRACT FROM PUBLISHER]

Traffic fatalities are the leading cause of death among the young and middle-aged population in Estonia. The objective of this study was to reveal the pattern of traffic fatalities among the population aged 15 - 64 years and to determine the role of alcohol in their fatalities. The data were collected from post-mortem reports at the Estonian Bureau of Forensic Medicine from 2000 to 2002. Alcohol-related deaths were those with a blood alcohol concentration (BAC) equal or above 0.05 g/100 ml. Out of 512 victims, 401 were males and 111 were females. The greatest group were car occupants (58%) followed by pedestrians (31%). The portion of alcohol-related deaths was 70% among men and 44% among women. The mean BAC and percentage of alcohol-related deaths was significantly higher in pedestrian than in driver fatalities. Alcohol intoxication was identified as the most powerful contributing factor to traffic fatalities. The results provide more evidence for politicians to tackle alcohol abuse and unsafe traffic environments. [ABSTRACT FROM AUTHOR]

This article provides a technical evaluation of the traffic control element of the Anaheim Advanced Traffic Control System Field Operations Test (FOT), sponsored by the US Department of Transportation. The primary objective for this test was the evaluation of adaptive traffic signal control technologies, including the Split Cycle and Offset Optimization Technique (SCOOT) for intersection signal control. The SCOOT evaluation was defined relative to existing, first generation Urban Traffic Control System (UTCS)-based control using standard US field detectorization. This US geometry is not the detector configuration normally used with SCOOT. SCOOT was implemented with some degree of success, though technical problems limited its performance. Anaheim's existing communication and controller systems contributed major deployment limitations since they were less adequate than anticipated. SCOOT remains in use in selected areas, with plans for system expansion. [ABSTRACT FROM AUTHOR]

City logistics aims globally to optimize logistics systems within an urban area by considering the costs and benefits of schemes to the public and private sectors alike. Private shippers and freight operators aim to reduce their freight costs, while the community attempts to alleviate traffic congestion and environmental problems. City logistics initiatives attempt to minimize the total costs of freight movement within urban areas. Schemes for reducing the environmental and social costs as well as the economic costs are sought. This paper describes the development and application of mathematical computer-based models that have been used in the planning and evaluation of city logistics schemes. A detailed description of several modelling approaches that have been developed to predict the effects of specific city logistics schemes is presented. Integrated modelling approaches that combine both optimization and simulation, such as dynamic flow simulation and multi-agent systems, allow the effects of city logistics schemes to be predicted. [ABSTRACT FROM AUTHOR]