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Under the context of rapid development of the Internet of vehicles and vehicle-road collaboration system, active traffic management (ATM) becoming the mainstream means of road traffic control and developing toward refinement. In this paper, to study the high-precision lane-level dynamic induction control strategy in different scenarios, based on the NaSch model of cellular automata and combined with the characteristics of the failure section area, a fuzzy lane-changing bypass vehicle-following model considering lane-changing pressure in multi-lane failure scenarios was built. The simulation results show that (i) if the lane failure occurs on the middle lane, the lane should be induced in advance, and the induced lane change effect is the best at about 100 m. When the lane failure occurs in the left lane and right lane, the prompt is best at about 250 m. (ii) The induced distance should be based on actual traffic conditions, free combination of different early warning distances between 100 and 300 m can save about 20–30 s congestion time. (iii) The lane-level dynamic coordinated guidance control measures can effectively improve the road traffic efficiency compared with the static unified control measures, improve the traffic efficiency of road performance, and alleviate traffic congestion time. The conclusion of this paper can provide some reference for dynamic active control management and achieve higher accuracy of traffic flow lane-level control. [ABSTRACT FROM AUTHOR]

To contend traffic congestion on urban networks, existing studies have made great efforts to develop traffic-responsive signal timing algorithms in the last decade. More recently, as an alternative to conventional model-based algorithms, machine learning-based methods have been tested on traffic light timing problems and show promising potentials. However, many researchers and practitioners still questioned the feasibility and applicability of adopting machine learning techniques in the ATSC domain. One of the reasons is that these methods assumed flawless detectors and heavily relied on simulators for training and evaluations. To address such a critical concern, this article customizes a Deep Q-learning Learning (DQL) method to optimize traffic signal timings at urban intersections, where the partial observations from identity-based detectors are inputs, and the green splits are outputs. A simulation-free data-driven prediction model is also developed to train the DQL with reduced computational time. Then the machine learning-based methods are evaluated on a real-world case with Automatic Number-Plate Recognition (ANPR) data. Experiments show the proposed data-driven model can predict the traffic state in limited computational time, and the DQL algorithm is 3.9% better than the field experiment performance from the adaptive control system, SCOOT, and 22% better than the time-of-day plan by SYNCHRO. The results indicate the DQL methods can only yield marginal improvement with restrictive input and output settings in congested traffic flow in comparison to the conventional adaptive method. [ABSTRACT FROM AUTHOR]

Presently, most smart cities face massive traffic issues every day. The smart cities' significant challenge is the traffic control system, wherein some places are automated and cost-effective. In this manuscript, cloud-assisted Internet of things Intelligent Transportation System (CIoT-ITS) is proposed to overcome traffic management's challenges. Here, the IoT sensor integrated camera is installed in every traffic signal corner to monitor the vehicle's flow. Further, the optimised vehicle flow data is sent to the cloud processes. The data from the various signal corners runs an algorithm to detect traffic direction and controls the signal lights. The alert notification is sent to the nearest traffic control room during traffic congestion using IoT sensors. Simulation analysis proved that the proposed CIoT-ITS could monitor and manage the vehicle flow successfully and automatically. The proposed system has been validated based on the optimisation parameter, which outperforms conventional methods. [ABSTRACT FROM AUTHOR]

Actuated and pre-timed traffic signal controllers have been beneficial to the improvement of traffic flow in cities and dense urban environments around the world. While these methods have been effective in reducing traffic congestion, recent works have shown that incorporation of reinforcement learning (RL) or other artificial intelligence (AI) based optimization techniques may further improve the performance of traffic signal controllers. This work investigates a novel decentralized traffic signal control structure which encourages cooperative signal behavior via repeated negotiations between neighboring intelligent agents. This method capitalizes on emerging inter-infrastructure communications technologies to exercise 'system-level' control over a network of connected signalized intersections. The proposed method was tested in a simplified grid-network of 20 intersections. In this network, static arrivals of 1440 veh / l / h along east-west lanes and 360 veh / l / h along north-south lanes were supplied. In addition, a simulated shift to 720 veh / l / h along east-west lanes and 1080 veh / l / h along north-south lanes was analyzed to provide insights into the presented method's performance in response to any sudden shifts in traffic patterns. The findings indicated that, when compared to non-negotiating traffic signals, the presented method may improve the service rate of traffic networks under static conditions by 67 1 veh / h on average, reduce emissions by an average of 326 kg / h in addition to reducing travel time across a network of intersections. The performance characteristics were captured by a SUMO testbed, and computational efficiency was explored using a suite of simple testbeds developed in MATLAB. [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]

The current non-connected autonomous vehicle scheme for speed changing along the road has limitations. Other alternatives require a central control or a complex communication system between the vehicles. We suggest a low cost and simple method for controlling the traffic of a line of autonomous vehicles using predetermined speed profiles imposed upon the vehicles along the road. We introduce a novel method to control autonomous vehicles traffic. Particularly, we investigate cases where specific velocities are required at some points along the road. This is done by comparing different velocity profiles for acceleration, deceleration or a combination of both. As traffic flow and speed limit may change due to upcoming road conditions, it is imperative to control vehicle line traffic, such that phantom jams will be prevented while preserving maximal traffic flow at minimum energy. We provide a comparison of these profiles for acceleration, deceleration, and for the combined case in terms of traffic flow, energy consumption, travel duration and the resulting jam characteristics. Following the comparison, we conclude that the best strategy would be to use linear speed profiles both to accelerate and to decelerate. Lastly, we suggest a tool to compare speed profiles for deceleration cases where the formation of an upstream propagated traffic congestion is inevitable. [ABSTRACT FROM AUTHOR]

The rapidly-increasing urban transportation contributes to a number of externalities such as congestion and pollution; moreover, environmental uncertainties widely exist and bring challenges to creating traffic emission control strategies. To address these problems, this paper proposes an inexact bilevel programming approach under stochastic and fuzzy uncertainties (BLP-SF) for a toll scheme design with the considerations of the externalities of vehicular emission and road pricing policy. The BLP-SF approach can deal with multiple environmental uncertainties by (1) specifying the uncertainties as probability distributions and/or fuzzy sets, and (2) integrating chance-constrained programming and fuzzy possibility programming into the bilevel programming framework. A road pricing problem is exemplified under various policy scenarios to demonstrate the applicability of the BLP-SF approach. It is shown that the proposed BLP-SF approach can achieve optimal charging schemes with improvements in total emission reduction and congestion alleviation compared with traditional models. This study contributes to urban transportation management associated with congestion and emission. [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]

Cooperative Automated Driving System (ADS)-equipped Vehicles (CoAVs) can be a promising solution to traffic congestion. We present a reinforcement learning strategy using Genetic Fuzzy Systems (GFS) for cooperative merge of vehicles onto a highway in high density, mixed-autonomy traffic conditions. The CoAVs are trained to make their own decisions purely based on information available from its surrounding vehicles, thus making this a decentralized system. The GFS module in each CoAV makes recommendations on the acceleration and lane-change decisions. The CoAVs are trained on different ADS behavioral parameters, such as aggressiveness of lane change, different CoAV market penetration rates (MPRs) and traffic congestion levels. The results show the effectiveness of increasing the MPR. It is noticed that the CoAVs trained with different ADS behavioral parameters can generate completely different cooperative maneuvers. The results also show that the trained CoAVs can cooperate even with human-driven vehicles to improve the overall traffic performance. [ABSTRACT FROM AUTHOR]

Efficient machine learning algorithms capturing both spatial and temporal dependencies offer promising results for many fields including traffic prediction. However, their performance is still limited, particularly in congested areas where the speed on a road varies significantly. This paper exploits spatial (congestion) dependencies in road networks to improve the performance of spatio-temporal deep learning algorithms. Essentially, we extract spatial information relevant to how congestion develops and evolves in the network and enhance deep learning algorithms with it by developing custom prediction models for various network components. This research identifies traffic patterns through graph theory and traffic flow fundamentals, integrates them with deep learning algorithms for traffic prediction purposes, and enables better predictions in critically congested areas of road networks. The case studies with New York and Amsterdam networks show promising results; the proposed enhanced models significantly outperform the original deep learning models that consider the whole network as the prediction domain. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of road pricing for the bottleneck model with endogenous random delay. We extend the widely used scheduling model by integrating trip scheduling, endogenous traffic congestion, travel time variability (TTV), and road pricing into one modeling framework to analyze the role of road pricing in addressing TTV. This paper investigates the departure profiles and examines the social surplus under TTV and derives analytical results to illustrate properties of the model. It demonstrates the impact of road pricing and TTV on the departure profile of travelers and the system performance. For travelers, the presence of TTV leads to a smooth departure profile, hence a reduction in queuing cost. As for the system, TTV reduces the social surplus. This study shows that road pricing can be used to compensate or mitigate the social surplus loss due to TTV. [ABSTRACT FROM AUTHOR]

When multiple equilibria exist, the desired traffic equilibrium, may not be reached through a day-to-day dynamic adjustment process. Han et al. ["Discrete-Time Day-to-Day Dynamic Congestion Pricing Scheme Considering Multiple Equilibria." Transportation Research Part B: Methodological 104: 1–16.] proposed a day-to-day dynamic pricing scheme charged on all links to ensure the desired traffic equilibrium can be achieved even when multiple equilibria exist. However, due to many practical constraints, the second-best pricing scheme has more practical interests. This study first discussed the issues of existing congestion pricing schemes for second-best case. Then, this study developed a second-best dynamic road pricing scheme that is implemented on a dynamic subset of the road links in the network. Our rigorous theoretical proof and numerical tests prove that the dynamic pricing scheme can drive the traffic state evolution towards the desired second-best traffic user equilibrium state from any initial traffic state. [ABSTRACT FROM AUTHOR]

A road construction project has a significant effect on traffic congestion and operational performance of the project corridor and connecting street links. However, not many researchers focused on travel time performance-based measures to assess the effect of a road construction project on the project corridor and connecting street links. Therefore, travel time data for a freeway road resurfacing project on 100 links within its vicinity were gathered and processed to assess the effect on freeway and connecting arterial street links. A paired t-test was used to examine the differences in means between the before and during road resurfacing project period travel time performance-based measures. There is a significant difference in travel time performance-based measures before and during the road resurfacing project period on freeway links and connecting arterial links. Their significance varied by the time-of-the-day and day-of-the-week on the freeway and connecting arterial links. The average travel time, planning time, and travel time index can consistently explain the effect of a road resurfacing (construction) project on transportation system performance when compared to buffer time and buffer time index. The findings can be used to enhance temporary traffic control practices in the vicinity of road construction projects. [ABSTRACT FROM AUTHOR]

Traffic congestion has become a significant issue in urban road networks. There have been massive works about traffic signal optimization to improve the efficiency of traffic flow operation, and the so-called back-pressure control policy has proven to be excellent for oversaturated conditions. Most of the existing works with back-pressure are based on an adaptive phase sequence, and research with cyclic phase sequence is based on calculating the splits for different phases using the traffic flow data at the beginning of each cycle, which is unfair for the non-initial phases. In this paper, we propose a decentralized model predictive signal control method with fixed phase sequence using back-pressure policy. The main idea of the new method is to form a control loop using the model predictive control, enabling the system to obtain real-time feedback from the traffic network and dynamically adjusting signal timing plans at the beginning of each phase. As links within a certain area have various lengths, the same queue length can imply different traffic conditions, so a method to normalize queue lengths is proposed. The normalized queue length decreases drastically when the actual length approaches link capacity, thus avoiding spillover. The proposed method was tested in a virtual road network. Numerical results suggest that the new method improves performance under congested conditions in terms of throughput, Gini coefficient and comprehensive transportation efficiency. [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]

In this paper, two new adaptive traffic signal control algorithms are proposed based on data from probe vehicles to realize the coordinated signal control of arterial roads. One is an iterative signal control algorithm, and the other is an optimized signal control algorithm. The proportion of vehicles in the nonstop group is selected as the indicator of the traffic state. The value for this indicator can be accurately estimated by data from probe vehicles. Our goal is to ease traffic congestion and enhance the capacities of traffic networks. Compared with the Webster fixed-time signal control algorithm, these two new adaptive signal control algorithms are evaluated on a microscopic simulation platform. Simulation results show that the average travel time is reduced by 32% under the iterative signal control algorithm and by 23% under the optimized signal control algorithm, and the average delay times are reduced by 36% and 35%, respectively. In the meantime, the average number of stops under the iterative signal control algorithm is reduced by 43%, and under the optimized signal control algorithm, by 67%. They indicate that the two new adaptive signal control algorithms are effective for easing traffic congestion and achieve the adaptive signal control objectives using real-time traffic information. [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 paper analyzes the changes in spatial–temporal job accessibility by car in the Netherlands during the economic crisis (2009–14). It also assesses which component change is the most determinant in accessibility changes per municipality and part of the day. The paper shows that changes in job distribution reduced accessibility in almost the entire country, except around Amsterdam. Improvements in the road network capacity increased accessibility in the central provinces, particularly during peak hours. In summary, the values of job accessibility by car in the Netherlands became more transport dependent, except in the Amsterdam region. [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]

As traffic congestion intensifies, identification and control of critical areas to balance road network traffic flow are of paramount importance to mitigate and prevent traffic congestion, based on the road network traffic realities. Most previous traffic sub-region division methods of static characteristics are not suitable for dynamic traffic management. This article proposes the division model of traffic sub-regions and identification method of critical control areas based on macroscopic fundamental diagram (MFD) theory closely integrated with dynamic characteristics of road network traffic. To be specific, the existence of small-scale road network MFD is confirmed at first through the traffic data transmitted from Jiangbei district in Chongqing, China. The initial control sub-regions of the road network are formulated with each small-scale road network as a control area. Then, the traffic state value of sub-regions is calculated by means of regression analysis. A set of rules are designed to aggregate the initial sub-regions, which are associated with the traffic state and geographically adjacent, and the adjusted traffic sub-regions are obtained by means of correlation coefficient analysis. Finally, the identification method of traffic critical control sub-regions is proposed based on clustering analysis. In addition, the identification model of critical control sub-regions is applied and analyzed in the experimental road network of Jiangbei district in Chongqing, China. The results demonstrate that the proposed model is flexible and efficient enough to improve the control over road networks, promoting application of traffic sub-region division based on MFD in dynamic traffic control over actual road networks. [ABSTRACT FROM AUTHOR]

Traffic congestion is one key factor that delays emergency supply logistics after disasters, but it is seldom explicitly considered in previous emergency supply planning models. To fill the gap, we incorporate traffic congestion effects and propose a two-stage location-allocation model that facilitates the planning of emergency supplies pre-positioning and post-disaster transportation. The formulated mixed-integer nonlinear programming model is solved by applying the generalized Benders decomposition algorithm, and the suggested approach outperforms the direct solving strategy. With a case study on a hurricane threat in the southeastern USA, we illustrate that our traffic congestion incorporated model is a meaningful generalization of a previous emergency supply planning model in the literature. Finally, managerial insights about the supplies pre-positioning plan and traffic control policy are discussed. [ABSTRACT FROM AUTHOR]

Numerous cities around the world are considering the implementation of road pricing to ease urban traffic congestion, following on from the success in cities such as London and Singapore. However, policy-makers are also all too aware of the generally negative public opinion toward such measures. This study makes use of data collected in four cities (two in Sweden, one in Finland, and one in France) using a very consistent survey probing for citizens' attitudes toward pricing. We find very strong similarities across the four cities in terms of a number of underlying attitudinal constructs that help explain people's answers in a hypothetical referendum on congestion pricing. The similarities across cities indicate that the increase in the opinion toward congestion pricing once they are introduced is not primarily an effect of changes in underlying attitudes, changes in how the underlying attitudes influence the support for congestion pricing, or differences in anticipated versus experienced or perceived self-interest. Instead, this effect seems to be caused by a status quo acceptance, tending to increase the support for the current situation. [ABSTRACT FROM AUTHOR]

This paper describes how the Taxi Passenger Enhancement Program (TPEP) Trip Record data can be used to measure traffic congestion along selected corridors in Manhattan. Two corridors in the rapidly changing Far West Side, Ninth Avenue and Tenth Avenue, were selected for analysis in this case study. This paper will describe a methodology using GIS applications that can visualize and identify trends in traffic conditions over time along corridors selected for analysis. This methodology can be applied to evaluate and monitor congested corridors in response to changes in land use patterns, policy decisions, and various temporary conditions affecting street operations. [ABSTRACT FROM AUTHOR]

Public transport (PT) disruption can occur due to various factors such as malfunctions and breakdowns of vehicles, power outages, and personnel strikes. This paper explores the network-wide impacts of PT strikes (train, tram, and bus strikes) on traffic congestion in Melbourne, Australia using a network modeling approach. A primary survey aimed to investigate the mode shift of users when each public transport mode ceases was conducted with 648 public transport users in May 2016. Findings show that train withdrawal was expected to result in 43% of users shifting to car. Smaller yet significant shifts to car was also expected with bus withdrawal (34%) and tram withdrawal (17%). Based on the survey results and the use of a four-step transport model, train withdrawal was expected to increase the number of severely congested road links by 130% and reduce the average travel speed from 48 km/h to 39 km/h (20% decrease). Bus and tram withdrawal was also found to increase congestion although the result was less severe. Future research should investigate the switching behavior in actual withdrawal events and explore the long-term effects of public transport withdrawal. [ABSTRACT FROM AUTHOR]

This paper develops an improved cellular automaton model in congested traffic conditions that differentiates vehicle combinations to consider both vehicle physical performance and driving behavior difference in car-following behavior. Car-following is divided into four groups by the lead and lag vehicle type. The vehicle behavior, including steady space gap, acceleration, and behavior choice, was studied to find out the diversity of car-following behavior in different vehicle combinations. The results show that vehicles in different vehicle combinations have different carfollowing behaviors. In the proposed model, each vehicle combination has its particular update rules and parameters. The model was evaluated by comparison with the existing car-following models at the microscopic and macroscopic level and the stability was also analyzed. The results show that the improved cellular automata model has a satisfactory performance. [ABSTRACT FROM AUTHOR]

There is a need for gating in some parts of an urban network that have higher priority especially during peak hours. This article presents a developed model, which we named internal-external traffic metering strategy (IETMS), begins with maximizing the offset in entering arterials of protected sub-network. The first step requires an accurate evaluation of optimal queue length in upstream arterials. In the second step, traffic light schedules are optimized in entering arteries. This optimization is done based on maximizing the traffic entry flow of arterials to the sub-network, keeping queue length close to its optimal amount and preventing extreme oscillations in green time through cycles. The congestion rate inside the protected area will remain below its critical value in this condition. Results show that using the novel IETMS model not only causes gating in the subnetwork but also improves total throughput of the network in such a way that the average speed and delay have had 14 and 19% improvement, respectively. [ABSTRACT FROM AUTHOR]

Travel-time information is an integral part of Advanced Traveler Information Systems and Advanced Traffic Management Systems. Real-time estimation of stream travel time will be helpful in making trip decisions such as route choice and departure time. The present study proposes a method to predict stream travel time using particle filtering approach which considers the predicted stream travel time as the sum of the median of historical travel times, random variations in travel time over time, and a model evolution error. The present model hypothesizes the median of historical travel times obtained from the collected data as a priori estimate and hence predicting the actual travel time will be equivalent to forecasting the variations in travel time according to the current measurements. In order to capture the random variations in travel time, a dynamic mathematical modeling approach with particle filtering technique is used. The results obtained from the implementation of the above method are compared with the measured travel time data and the prediction accuracy, quantified using the Mean Absolute Percentage Error and Mean Absolute Error was observed to be satisfactory. Performance of this method was compared to a basic speed-based approach, where travel time is obtained by time-distance relationship using space-mean speed, and the proposed method showed better performance. [ABSTRACT FROM AUTHOR]

To understand the congestion problem and the occurrence of bottlenecks and to formulate solutions for it, a thorough study of vehicle-to-vehicle interactions is necessary. Car-following models replicate the behavior of a driver following another vehicle. These models are widely used in the development of traffic simulation models, and in analysis of safety and capacity. In India, traffic on roads is mixed in nature with wide variations in physical dimensions and other vehicular and traffic characteristics with loose lane discipline. In mixed traffic conditions, leader-follower vehicle types are not only car-car cases but also there are different combinations of vehicles (e.g. car-two wheeler, two wheeler-auto rickshaw, and heavy vehicle-two wheeler). The present study focuses on evaluation of different vehicle-following models under mixed traffic conditions. The car-following models such as Gipps, Intelligent Driver Model (IDM), Krauss Model and Das and Asundi were selected for this study. These models were implemented in a microscopic traffic simulation model for a mid-block section. The performance of different vehicle-following models was evaluated based on different Measure of Effectiveness (MoE) using field data collected from a four-lane divided urban arterial road in Chennai city. Speed-concentration and flow-concentration relationships for different vehicle-following models were developed and analyzed for different compositions. Capacity is higher when the proportion of smaller size vehicles is higher, since these vehicles use longitudinal and lateral gaps effectively. The simulation model was also applied to evaluate a range of traffic control measures based on vehicle type and lane (Ex: exclusion of auto-rickshaws, heavy vehicles, auto-rickshaws + heavy vehicles, etc.). The results showed the promise of some measures based on vehicle class, namely, the exclusion of auto rickshaws or auto rickshaws and heavy vehicles. The findings have interesting implications for capacity and PCU estimation and Level of Service (LoS) Analysis. [ABSTRACT FROM AUTHOR]

This paper investigates congested traffic loading on long-span bridges through the use of traffic microsimulation. Six months of Weigh-In-Motion free-flow traffic data (including cars) are used as input for the microsimulation of congested traffic. Key parameters that affect traffic loading are identified in the output of the microsimulation, and these parameters form the basis for a more computationally efficient ‘pseudo-microsimulation of congested traffic’ (PMCT) model. This PMCT model is shown to replicate the traffic loading from full microsimulation accurately and allows long-run simulations, equivalent to 1000 years of congested traffic, to be performed with an acceptably short duration. This reduces the significant uncertainties associated with extrapolating short-run simulation results to long return periods. The 1000-year simulated results from the PMCT are compared with the extrapolated results from full microsimulation, and with the traffic loading from some design codes, for different bridge lengths. Both types of microsimulation are also applied to calculate maximum lifetime loading for two typical long-span bridges – one cable-stayed and one suspension bridge – using influence lines determined from finite-element models. [ABSTRACT FROM AUTHOR]

It is intended that the pavements need to be designed to accomplish better performance due to the continuous increases of traffic volumes, vehicle loads and tyre pressure, especially for the channelized traffic. To achieve this objective, the method of accelerated pavement testing is used to directly optimize the asphalt pavement design. Therefore, the quality of the accelerated pavement testing facility plays an important role in optimizing the pavement design. In order to improve the success rate of the facility, the mechanical analysis of the accelerated pavement loading facility based on multi-rigid-body dynamics is carried out before the design and fabrication, so as to establish the virtual prototype of loading carriage; the multi-body dynamic equations are obtained according to the Newton-Euler formula; the force and displacement of each joint are simulated by using the MSC ADAMS software, and the results of stimulation meet the requirement; a full-scale accelerated loading testing (ALT) system with independent intellectual property right is developed. The ALT can be operated in single-axle or dual-axle loading mode, which is better than the existing facilities. The experiment indicates that the quality of the ALT, which is analysed based on multi-body dynamic, is excellent. [ABSTRACT FROM AUTHOR]

Short period traffic counts (SPTCs) are conducted routinely to estimate the annual average daily traffic (AADT) at a particular site. This paper uses Indian traffic volume data to methodically and extensively study the effect of four aspects related to the design of SPTCs. These four aspects are: (i) for how long, (ii) on which days should SPTCs be carried out, (iii) how many times, and (iv) on which months should SPTCs be carried out? The analyses indicate that the best durations for conducting SPTCs are 3 days (starting with a Thursday) and 7 days, for total traffic and truck traffic, respectively. Further, these counts should be repeated twice a year keeping a separation of two months between the counts to obtain good estimates of AADT at minimal cost. An additional outcome of this study has been the determination of seasonal factor values for roads in developing economies, like India. [ABSTRACT FROM PUBLISHER]

This research studies effects that the average increase in travel times due to road traffic congestion has on characteristics of an existing distribution network. It presents the most detailed estimate of ‘on-the-road’ effects on distribution network characteristics up to now, from the network modelling perspective and from the processed data point of view. A concrete network model allowing for the representation of all relevant transportation flows is presented. The processed traffic information relies on navigation service data. The use of such data allows the requirements that arise for the traffic analysis of a whole distribution network to be met. It is shown that this data source may considerably contribute in forthcoming research. The effects of traffic congestion are quantified to get insights into the extent to which regular traffic congestion affects distribution network characteristics and to understand the mitigating effect when the number of distribution centres is increased. [ABSTRACT FROM PUBLISHER]

In December 2008, the Greater Manchester electorate voted to reject a £3 billion package of transport measures that would have included investment in the conurbation's bus, tram and rail networks and walking and cycling infrastructure, together with, and partially funded by, the introduction of a congestion charge. The proposals followed a successful bid to the UK Government Transport Innovation Fund (TIF). High levels of car use present challenges to cities, and the TIF bid can be seen as an attempt to address these by promoting and facilitating a modal shift. The paper reflects on the debates surrounding the proposals, which led to a referendum. In particular, it explores the challenges of communicating complex, controversial plans in a fragmented and contested political arena. [ABSTRACT FROM AUTHOR]

This article investigates the effects of congested freeway traffic conditions on motorists’ exposure to traffic-related air pollution using real-world traffic data and a framework of established emissions and dispersion models. The intent is to isolate and compare the influences of congested traffic characteristics on exposure duration and exposure intensity. Mass inhalation of carbon monoxide (CO) and nitrogen oxides (NOx) is estimated for 45,226 simulated trips through a 14-mile congested freeway corridor. Results show that congestion increases total trip pollutant inhalation primarily through motorist delay (exposure duration), and to a lesser extent through increased concentrations (exposure intensity). The effects of varying wind and background concentration can be sufficient to obscure the influence of congestion on exposure intensity. The variability of exposure intensity due to traffic is mitigated by offsetting impacts among traffic flow, emissions rates, and pollutant dispersion. Exposure intensity increases with higher traffic flow and with lower traffic speed, but the impact of lower traffic speeds (through increased emissions rates and decreased dispersion) is smaller. The importance of exposure “hot spots” at traffic bottlenecks also increases in congestion. These findings suggest thattraffic-basedmotorist exposure mitigation should focus on reducing travel duration on high-volume corridors through reduced vehicle flows (i.e., demand-side congestion mitigation). This analysis does not include non–congestion-based mitigation strategies such as cleaner vehicle engine technology or improvements in vehicle cabins, which can also reduce exposure intensity. On an individual scale, motorists can greatly reduce their own exposure during travel by, among other strategies, adjusting their departure time to less congested, lower volume periods. [ABSTRACT FROM AUTHOR]

Variable speed limit (VSL) systems have been more widely implemented in Europe compared to the United States. Studies have demonstrated positive safety impacts of such systems; however, there are very few studies that used actual deployment data to investigate the operational benefits of VSL systems. This article presents the operational impacts of a VSL deployment on Interstate 270 in the state of Missouri in the United States. Techniques such as parametric curve fitting, nonparametric methods, and other statistical tests were used to identify the changes between before and after traffic conditions. The effect of VSL on traffic performance was investigated at eight heavily congested locations. The two-dimensional Kolmogorov–Smirnov test results indicated that flow–occupancy diagram changes were statistically significant at seven out of eight locations. The slopes of flow–occupancy plots for over critical occupancies were found to be steeper after VSL. Slight changes in critical occupancy were observed after VSL implementation. However, the changes were inconsistent across locations, with some witnessing an increase and others witnessing a decrease. The maximum flow prior to breakdown (the prebreakdown flow) decreased at four locations and increased at four locations after VSL. The maximum flow after breakdown (the postbreakdown flow) decreased at three locations and increased at five locations after VSL. The average daily duration of congestion decreased at five locations and increased at three locations after VSL. Findings of this study help to develop VSL control algorithms that are more efficient in improving VSL traffic operations benefits. [ABSTRACT FROM PUBLISHER]

Aims: The aim of the study was to make a theoretical contribution by investigating social cognitive factors and personality traits related to risky driver behaviour in a Turkish sample. The study tested three theoretical models by Structural Equation Modelling: (1) a model using risk perception and attitudes towards traffic safety to predict driver behaviour, (2) a model which used normlessness and sensation-seeking traits to predict such behaviour and (3) a model which used both the social cognitive factors and personality traits to predict driver behaviour.Methods: A questionnaire survey with validated measurement instruments was conducted in a sample of Turkish drivers (n = 213). The response rate was 61%.Results: A combined social cognitive and personality trait model had tolerable fit and explained 24% of the variance in driver behaviour. The relations between personality traits and risk perception with driver behaviour were mediated through attitudes towards traffic safety. Risk-taking personality traits had relatively strong relations to unsafe driver behaviour, whereas risk perception had a relatively weak relation to such behaviour.Conclusions: A combined social cognitive and trait approach may be efficient in human factor campaigns aimed to reduce risky driver behaviour in Turkey. Personality traits may be important for driver behaviour because they influence the attitudinal determinants of such behaviour. Increased police enforcement of road traffic regulations may reduce risky driving among individuals with normlessness and sensation-seeking traits. Personality traits may also guide efforts aimed at early identification of risky drivers and campaigns could be tailored to specific personality characteristics. [ABSTRACT FROM PUBLISHER]