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Traffic congestion is one of the most common problems in the transportation system. In urban planning and construction, traffic congestion increases the difficulty of control and scheduling, hindering the pace of urban modernization development. The development of intelligent transportation systems has to some extent solved these problems, among which traffic flow prediction is particularly crucial. A period processing layer containing concatenation, dimension amplification, and diffusion convolution was studied and designed. On the grounds of this, a graph neural network prediction model combining spatiotemporal period characteristics was designed. And it further introduced a traffic flow prediction method on the grounds of dynamic topology maps. This model combined adaptive topology generation and parameter learning mechanisms, synchronously processing the complex correlation of traffic data and the heterogeneity between nodes through gated recurrent neural networks. The outcomes indicated that the research model is more excellent than other graph learning methods in three evaluation indicators: mean square error, mean absolute percentage error, and root mean square error. Their values were 15.63%, 9.87%, and 25.08%, respectively. The evaluation on all four datasets showed excellent performance of the research model, confirming the effectiveness of its adaptive topology and parameter learning strategy. This study had important practical significance for improving the traffic management system, alleviating traffic pressure, and enhancing the travel experience of urban residents. [ABSTRACT FROM AUTHOR]

The article offers information on the Dominican government's presentation of the Santo Domingo Integrated Transportation System (SIT-SD), a project addressing traffic congestion and mobility issues in the capital city, with an estimated investment of 3.2 billion dollars. Topics include the initiative's focus on reducing private vehicle traffic and promoting collective transportation by connecting the entire metropolitan region of Greater Santo Domingo through a public-private partnership model.

The continuous growth of the economy and population around the world has led to an increase in transportation demand. Consequently, the number of vehicles keeps increasing to satisfy the continued demand for transportation. Traffic congestion and delay, as a result, is becoming the norm in many big cities. The first step to alleviate the traffic congestion and delay is to gain a better understanding of the traffic operations at the city’s road network. This study tries to investigate and report about the traffic operations at urban roadways in Basra City, Southern of Iraq. With the focus of studying some of the main traffic parameters such as traffic flow, speed, travel time and delay at some selected sites with interrupted traffic flow within Basra City. Field traffic data from 30 roadways sites has been collected by using two techniques, camcorders and floating car technique. Data analysis showed that most of the selected sites are running under their capacity with an average speed close to the posted speed limit. However, the analysis of data also showed that for the majority of times on these selected sites long travel time and traffic delays were experienced, with an average delay of around 3.0 minutes for each 1.0 km road length. This could be attributed to deficiencies in the operation of traffic signals at intersections, presence of illegal on-street curb parking (with double parking sometimes) and the absence of traffic enforcement controls (with associated penalties). [ABSTRACT FROM AUTHOR]

The aim of this manuscript is to detect recent trends in air transport research through a bibliometric analysis. We retrieved all articles published between 2013 and 2022 in the Q1 of the Transportation ranking of the Social Sciences Citation Index published in the Journal Citations Reports (Q1-T), and in the Journal of Air Transport Management. Among these records, we retained for the analysis the articles related to air transport. Analyzing the author keywords of articles and retaining the Top 10 cited articles in each of the samples, we identified six research topics for each of the samples. Both samples share the topics of Industry Analysis, Air Traffic Management and COVID-19 and Air Transport. The specific topics of Q1-T sample are High Speed Rail and Air Transport, Environmental Impact of Air Transport and UAV and Urban Air Mobility. We also found that materials of Q1-T are mainly written from the regulator perspective, and JATM materials from the perspective of airport and airline managers. We believe that these results are useful for administrators evaluating air transport academics, and air transport academics themselves looking for new avenues of research and stakeholders of the air transport industry interested in evidence-based decision making. [ABSTRACT FROM AUTHOR]

To enhance the control effect of intelligent transportation system (ITS) of expressway, avoid blind travel causing traffic congestion, improve road capacity, and achieve the purpose of smooth and efficient operation of the network, 3σ criterion and 2σ cycle removal criterion were used to eliminate the abnormal data of travel time. Thereafter, a combined prediction model of Long Short‐Term Memory (LSTM) based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and attention mechanism is proposed. Travel time data, collected from the real world, is decomposed into several Intrinsic Mode Functions (IMFs) and a residue with the CEEMDAN. Then, the decomposed sub‐sequence is predicted and the final prediction result is obtained by comprehensive superposition. The results indicate that: the CEEMDAN algorithm has significantly improved the prediction performance. Compared with the AT‐LSTM model, mean absolute percentage error (MAPE) is reduced by 10.75%, 24.84%, 31.80%, 24.98%, 14.29%, and 31.42% when the time window was 3, 6, 9, 12, 15, and 18, respectively, in the dataset of Yaxi expressway. In the dataset of Chengnan expressway, the number of error points with MAPE greater than 40% is significantly reduced after the adoption of the LSTM model. After the introduction of CEEMDAN algorithm, the error at each time point is controlled within 30%. The proposed model controls the error at each time point within 25%. In different datasets, the proposed model has better prediction ability for highly non‐linear travel time, and can better capture the trend abrupt characteristics of rush hour traffic flow. [ABSTRACT FROM AUTHOR]

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Current traffic congestion and the resulting carbon emissions are two of the main problems threatening the sustainability of modern cities. The challenges facing today's cities focus primarily on the optimization of traffic flow and the transition to electric vehicles. The latter aspect implies the need for an adequate deployment of the infrastructure of charging stations. The inherent complexity in today's cities and the difficulty in implementing new policies whose benefits are difficult to measure and predict has led in recent years to consider the enormous potential of simulation tools and in particular of the agent-based simulation (ABS). ABS allows the specification of complex models that reflect the complexity and dynamism of urban mobility. Current technology in ABS has evolved and matured sufficiently to provide very sophisticated tools but lacking facilities for a flexible and realistic generation of input data in the execution of the experiments. In line with this, this paper introduces two configurable generators that automatize the creation of experiments in agent-based simulations. The generators have been developed with the SimFleet simulation tool enhancing the simulation of realistic movements and location of vehicles, passengers and other users of the urban traffic system within a city. The generators proved to be useful for comparing different distributions of locations as well as different agent movement behaviors based on real city data. [ABSTRACT FROM AUTHOR]

This study aims to cognize the current effects of land use decisions on traffic in the city centre and predict the future effects of these decisions. The researchers studied and analyzed the effect of changing land uses in the eastern part of Al-Kut city centre on traffic in the city centre. The study adopted the descriptive analytical approach based on the study area data and information. A field survey of the land uses that are most attractive to movement and another field survey of the traffic volume in the study area's main streets. The study found that the transportation problems in the city centre will be exacerbated in the future due to the inappropriate position of many activities in the past two decades. All these changes attracted many transport trips and thus increased the volume of movement in the area. Kut city's specificity exacerbated the problem as all the crossings between the two river banks are located in the city centre. This problem will be exacerbated in the future when Wasit University colleges are completed, and the number of transport trips to them increases and at completing the mall construction. [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]

Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the growing accessibility of traffic-related data, we suggest that congestion can be instead captured by exogenous variables, such as travel time observations. We propagate the traffic flow based on an exogenous travel time function, which has a piece-wise linear form. Given piece-wise stationary route flows, the piece-wise linear form of the travel time function allows us to use an efficient event-based modelling structure. Our Data-Driven Network Loading (DDNL) approach is developed in accordance with the theoretical DNL framework ensuring vehicle conservation and FIFO. The first simulation experiment-based results are encouraging, indicating that the DDNL can contribute to improving the efficiency of applications where the monitoring of historical network-wide flows is required. Abbreviations: DDNL – Data Driven Network Loading; DNL – Dynamic Network Loading; DTA – Dynamic Traffic Assignment; ITS – Intelligent Transportation Systems; OD – Origin Destination; TTF – Travel Time Function; LTT – Linear Travel Time; DL – Demand level [ABSTRACT FROM AUTHOR]

This article presents an analysis of some smart cities worldwide, similarly shows in detail the concepts of artificial intelligence, governance and governance with the continuous support of the population, through various methodologies that try to meet the needs of the community in various fields such as the economic, mobility and the environment where they live. The city of Bogotá located in Colombia will be exposed as a specific case, where the respective technological achievements obtained in recent years will be shown to approach what should be an intelligent city, the faults that it has at the transportation level that is a great challenge for generated traffic jams and their possible solution through supervised neural networks, generating a simulation in the Road traffic simulator application for the data, likewise taking advantage of the approach that is being generated to make a transition in the traffic light of the city from incandescent bulbs to LED luminaire, which generates a lower consumption of electricity and a wide range of possibilities with respect to the internal control that can be given, to optimize traffic in the city and thus provide a solution to people who want a shorter time for your daily trips, the main motivation for the creation of this document is the proposal of a method with artificial intelligence that helps to improve the traffic flow of the city, since Bogotá is one of the cities with the most traffic in the world, as a result a rigorous evaluation of different neuronal architectures will be shown to alleviate the congestion of some streets of the city. [ABSTRACT FROM AUTHOR]

Accurate understanding and forecasting of traffic is a key contemporary problem for policymakers. Road networks are increasingly congested, yet traffic data is often expensive to obtain, making informed policy-making harder. This paper explores the extent to which traffic disruption can be estimated using features from the volunteered geographic information site OpenStreetMap (OSM). We use OSM features as predictors for linear regressions of counts of traffic disruptions and traffic volume at 6,500 points in the road network within 112 regions of Oxfordshire, UK. We show that more than half the variation in traffic volume and disruptions can be explained with OSM features alone, and use cross-validation and recursive feature elimination to evaluate the predictive power and importance of different land use categories. Finally, we show that using OSM's granular point of interest data allows for better predictions than the broader categories typically used in studies of transportation and land use. [ABSTRACT FROM AUTHOR]

The paper presents a comparison of a hybrid methodology which combines Singular Spectrum Analysis (SSA) with Artificial Neural Networks (ANN) against conventional ANN, applied on time series analysis and forecasting of road traffic volume. The main research objective was to develop a short-term forecast of daily traffic volume at toll stations across the Greek National Highway Network. The proposed methodology was implemented and evaluated upon a custom developed integrated forecasting software, based on the Mathworks MatLab platform. Experimental outcomes on daily data, from specific toll stations, demonstrate a superior prediction accuracy of hybrid SSA–ANN forecasting methodology against conventional ANN, when compared to performances of statistical criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R2). A comparison of results revealed that the SSA–ANN hybrid model could improve the forecasting accuracy of the conventional ANN model in the case of daily traffic volume forecasting. An Intelligent Transport System with embedded hybrid SSA–ANN forecasting algorithm could manage and analyze big data traffic volume time series in real time, providing an advanced decision support system for transportation system management and maintenance, while it would enable proactive decisions to mitigate the economic and environmental impacts of traffic congestion. [ABSTRACT FROM AUTHOR]

The traditional U-turn design has significantly improved traffic operations for relieving traffic congestion. However, the U-turn diversion and merge segments still cause traffic conflicts and delays. In this paper, an exclusive spur dike U-turn lane (ESUL) is proposed with the aim of addressing the disadvantages of the traditional U-turn design. ESUL provides a separate U-turn lane to diverge, decelerate, U-turn, accelerate and merge without interacting with through traffic. The effectiveness of ESUL is demonstrated through a field data investigation, simulation and analysis with VISSIM software. The proposed design is evaluated in terms of three parameters: travel time, delay and number of stops. Compared to the traditional U-turn design, ESUL can reduce travel time by 29.15%, delay by 66.70% and the number of stops by 100% at most. The results showed that ESUL has better performance than the traditional U-turn design and could be implemented to reduce traffic congestion and the potential hazards caused by U-turn maneuvers. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the results of the investigation of the three emission mitigation alternatives—upgradation to higher Euro norms, conversion to cleaner fuel and regulation of public transport, a major cause of interruption to traffic flow, from the private modes of transportation in a congested urban traffic corridor using both on-road measurements and emission modelling. The results show that the upgradation from Euro-III to Euro-VI emission standards for passenger cars could reduce up to 83-90% of CO, 86-92% of HC, 83-84% of CO2 during peak hours and 77% of NOx emissions during off-peak hours. The change of fuel from gasoline to CNG for auto-rickshaw could reduces up to 89% of HC and CO, 32-36% of CO2 during peak hours and 23-56% of NOx emissions during off-peak hours. And, by regulating the frequency of buses, emissions of CO2, CO and HC from both passenger car and auto-rickshaw reduced up to 40-66% during peak hours and NOx up to 40% during off-peak hours. The results, thus, showed that traffic flow management can bring significant emission reductions.Graphical abstract: [ABSTRACT FROM AUTHOR]

Abstract We study different mixes of private and public supply of roads in a network with bottleneck congestion and heterogeneous users. There are two parallel links for one origin and destination pair and two groups of travellers, where the group with the higher value of time also has higher schedule delay values. Previous scholars argued that as users become more heterogeneous, they benefit more from product differentiation, making private supply of roads more efficient. However, we find that local monopoly power might also increase if there is a 'separating equilibrium', which is an equilibrium where at least one group only uses one private road due to the different combinations of toll and congestion of the two roads. The private road can thus increase its toll without worrying about the competition from the other road for this group: it has a local monopoly over them. This lowers the efficiency of private supply. The problem is especially severe with flat tolls, which are constant over the peak. With fine tolls – which vary continuously over the day – there tends to be a pooling equilibrium – where both types use both roads – and competition remains intense. Flat tolling is also worse for users than fine tolling, as it has higher generalized prices. [ABSTRACT FROM AUTHOR]

Congestion is the obstruction of traffic flow which is one of the problems in developing countries. The main factor that causes congestion is the rapid population growth and lifestyle of the population. This observation aims to determine the factors that have caused congestion on BasukiRahmat Street, Kaliwates District, Jember Regency. Our data is obtained directly by using descriptive survey methods, which are supported by observation, documentation and description analysis techniques. The results of this observation show that rapid population growth and the lifestyle of the population who prefer to use private transportation have a significant influence on the congestion that occurs on BasukiRahmat Street, Kaliwates District, Jember Regency. According to the data we obtain from population growth and the lifestyle of the population, there needs to be self-awareness from the users of private transportation and the need for government support or attention to society and the environment. [ABSTRACT FROM AUTHOR]

The traffic during rush hour in metropolises is always in heavy congestion, so the wagon trucks of logistic transportation are mostly forbidden during rush hour. In fact, quite a few logistic transportations are fulfilled by converted passenger vehicles. This paper aims to model the logistic companies' behaviors and see through the effect of their cooperation modes on the companies and on the whole traffic conditions. The logistic companies decide their transportation pricing, warehouse using and route choosing separately or as a group (cooperation) to compete with others. It is also assumed that the heavy freight vehicles, light freight vehicles and the auto vehicles share the same network. Three models based on variational inequalities are proposed to describe the companies' behaviors when there is no cooperation, simple cooperation and deep cooperation, respectively, and the modified diagonalization method is used to solve these models. The total transportation demand is elastic, which depends on the users' choice of logistics company and further depends on all the companies' prices, warehouse and route choices. The goodness of different coalitions to the companies and the whole traffic system is well investigated in the numerical examples. [ABSTRACT FROM AUTHOR]

Abstract Uniqueness of the dynamic user-equilibrium assignment is still an important issue. This paper proves uniqueness with a milder condition compared to past studies and shows another counterexample to the uniqueness. A unidirectional network , in which any node on any shortest route has a unique node potential, is introduced. Orders of vehicles are determined by this node potential so that, given any two vehicles passing through the same node, the lower potential vehicle arrives at the node before the higher potential vehicle. It is shown that, for a unidirectional network in equilibrium, the link travel times and traffic volumes of congested links are uniquely determined. Moreover, a simple non-unidirectional network having multiple equilibria is introduced. This example exhibits importance of unidirectional-network structure to prove uniqueness. [ABSTRACT FROM AUTHOR]

Researchers have made significant strides in understanding car-following behaviour and traffic flow, especially with the advent of intelligent and networked technologies. Diverse mathematical models analyse traffic flow, each with pros and cons. This study focuses on a sensitivity-based mathematical model for uphill and downhill highways, examining position, velocity, and acceleration profiles to predict traffic jam occurrence. The model employs an ordinary differential equation and a machine learning-based approach (machine learning procedure neural network) for numerical solutions, exhibiting high accuracy (1 0 − 8 − 1 0 − 10) compared to the reference Runge–Kutta method For accuracy, reliability and stability of the results are evaluated by various performance indicators and statistical terms. For multiple independent executions, mean absolute deviation, root mean square error and error in Nash–Sutcliffe efficiency are calculated. Their values are lies in range 1 0 − 8 − 1 0 − 14 . Moreover, graphical analysis is established for better visualization of traffic flow and congestion. [ABSTRACT FROM AUTHOR]

Traffic congestion, a common and complicated phenomenon in urban transportation systems, is attracting increasing attention from researchers in Geographical Information Science (GIS) and other fields. In this study, we illustrate a general mechanism that reveals the relationship between travel time and dynamic traffic conditions. We measure a vehicle’s travel time to its destination along any path, where the travel time is calculated based on the path length and on the real-time traffic volume and transport capacity of each road segment on the path. On the basis of this measurement, we present a simple dynamic routing strategy that allows each vehicle to dynamically choose the path to its destination while imposing the minimum travel time. The application of our routing algorithm to the Chengdu street network, Barabási-Albert scale-free network and Erdös-Rényi random network shows that the proposed strategy remarkably improves network throughput and balances traffic load distribution. Our findings suggest that mining the time mechanism of network transport is important to explore efficient time-optimization routing algorithms to enhance the transport capacity of urban street networks and other kinds of networks. [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]

Simulating the congestion propagation of urban rail transit system is challenging, especially under oversaturated conditions. This paper presents a congestion propagation model based on SIR (susceptible, infected, recovered) epidemic model for capturing the congestion prorogation process through formalizing the propagation by a congestion susceptibility recovery process. In addition, as congestion propagation is the key parameter in the congestion propagation model, a model for calculating congestion propagation rate is constructed. A gray system model is also introduced to quantify the propagation rate under the joint effect of six influential factors: passenger flow, train headway, passenger transfer convenience, time of congestion occurring, initial congested station and station capacity. A numerical example is used to illustrate the congestion propagation process and to demonstrate the improvements after taking corresponding measures. [ABSTRACT FROM AUTHOR]

Public transit systems with efficient designs and operating plans can reduce both total costs and greenhouse gas (GHG) emissions. Total costs are a sum of user cost and agency cost. In previous work, we explored in various hypothetical city scenarios possible tradeoffs between GHG emissions and total costs for optimally designed transit systems. However, most current transit systems were not designed to minimize total costs. This study aims at finding the potential emission impact when optimizing the total costs of such a system, the MUNI bus network for the city of San Francisco. The continuum approximation (CA) method is used to obtain a stylized representation of the network. Total costs and emissions are derived as functions of system attributes such as headways and spacing. We analyze the optimal total costs and emissions and the corresponding attributes. Our result shows that a significant reduction in GHG emissions is achieved when total costs are reduced simultaneously. The cost-optimal MUNI bus system has a total cost of 0.15 billion $/year and emits 1 680 metric tons of greenhouse gases. These figures amount to about half of the cost and a third of the emissions in the current MUNI bus system. The optimal system has a lower spatial availability but a higher temporal availability of bus service than the current system, which highlights the potential benefits of providing more frequent express bus services. [ABSTRACT FROM AUTHOR]

Transit planning scenarios may lead to the different Objective Function (OF) values since each scenario has different transit travel times, frequencies and fleet sizes. Change on those variables leads to the different accessibility values for each route set. Therefore, the actual performance of a route set may be unforeseen since the accessibility values are out of evaluation criteria. This study tries to generate techniques, which handle the relation between accessibility and transportation in the scope of public transit. The accessibility measures, which have direct relation with land use and transportation, are utilized in transit route set decision. Accessibility measures have been utilized in the decision-making process of transit network design. Conventional OFs, which are used to determine the most effective route sets are combined with accessibility based OFs and the decision-making process of transit network design is strengthened. In this context, the effects of accessibility measures in decision-making process of transit network design have been represented on an 8-node example transit network. The results showed the accessibility measures could effectively improve the planners' decision accuracy. [ABSTRACT FROM AUTHOR]

At an isolated signalized intersection, short left-turn lanes may be provided to enhance intersection capacity and level of service. The capacity of an entire intersection depends not only on the effective green time per phase and the length of each short left-turn lane but also on the selected signal phase plan. This paper enumerates 16 typical signal phase plans for a four-leg intersection with protected left-turn phases. Given the existence of short left-turn lanes and the freedom to select signal phase plans, two optimization models are formulated. Numerical examples are carried out to illustrate the application of these models and show the sensitivity of the outcomes to impact factors. The orthogonal experiments show that the primary factor is whether a short left-turn lane is added on an approach and the demand distribution is the secondary factor in determining an optimal allocation of the time-space resources of an intersection. To analyse traffic flow operations under different signal phase sequences, the simulation tests are fulfilled using VISSIM under the assumption that the components of a signal phase plan and green splits are all identical. The simulated results indicate that the signal phase sequence for a specified approach may greatly affect traffic movements at an approach, and the leading green phasing is better than the lagging one for a specified approach when the left-turn bay length is short at the approach. Finally, two variations of the developed models are recommended for use and the procedure for the model application is provided in practice. [ABSTRACT FROM AUTHOR]

Cities around the world are inundated by cars and suffer traffic congestion that results in excess delays, reduced safety and environmental pollution. The interplay between road infrastructure and travel choices defines the level and the spatio-temporal extent of congestion. Given the existing infrastructure, understanding how the route choice decisions are made and how travellers interact with each other is a crucial first step in mitigating traffic congestion. This is a problem with fundamental importance, as it has implications for other limited supply systems where agents compete for resources and reach an equilibrium. Here, we observe the route choice decisions and the traffic conditions through an extensive data set of GPS trajectories. We compare the actual paths followed by travellers to those implied by equilibrium conditions (i) at a microscopic scale, where we focus on individual path similarities, and (ii) at a macroscopic scale, where we perform network-level comparison of the traffic loads. We present that non-cooperative or selfish equilibrium replicates the actual traffic (to a certain extent) at the macroscopic scale, while the majority of individual decisions cannot be reproduced by neither selfish nor cooperative equilibrium models. [ABSTRACT FROM AUTHOR]

The tramway is a mean of transport which is used frequently in many cities around the world, because it improves the offer of public transport through its important capacity of transporting. This may ameliorate the state of road traffic by reducing the number of vehicles along the tram line. Despite these advantages, there are some problems related to users' comfort like the noise caused during the operation. This study is focused on the human perception of the noise levels detected inside the tramway. The objective of this work is to identify and analyze the level of noise detected inside tramway, to know in which cabin and section the high noise is recorded, to find the causes and to show if the measured noise influences the comfort of passengers. For that, noise measurements are taken in all cabs and along the tram line. The results show that the levels of noise inside the seven cabins is different. The high noise is detected in the motorized cabins and in many sections, like the curves and also in other sections. The results show also that this noise affects negatively on the comfort of passengers. Finally, different solutions that can contribute to reduce effectively the noise to make the tram more comfortable and efficient to relieve traffic congestion in urban areas are presented. [ABSTRACT FROM AUTHOR]

Contemporary traffic engineering relies on effective management of transportation infrastructure to supply the variable mobility demands. Urban arterial congestion is a recurring problem that affects urban traffic flow. The degree of congestion may amplify if the arterial is a principal arterial that leads to a major bridge. Bosporus Bridge, inaugurated in 1973 and renamed in July 2016 as the Martyrs Bridge, is the first of the three suspension bridges connecting the Anatolian and European sides of Istanbul, Turkey. The Martyrs Bridge, along with the recently inaugurated Eurasia Roadway Tunnel provides a direct connection between the southern districts of the Anatolian and European sides of Istanbul for roadway traffic excluding trucks. Recurring congestion observed along the 5·7 km long westbound approach arterial from the Anatolian side of Istanbul to the bridge initiated a study to investigate methods to mitigate congestion through traffic management and improved roadway parameters. A micro simulation model based on traffic data suggested the applicability of traffic speed management along the existing infrastructure to relieve congestion and provide flow stability. The micro simulation model also provided the means for parametric evaluation of the effects of heavy vehicles, lane-width values and interchange spacing on the arterial flow speed and delay. [ABSTRACT FROM AUTHOR]

Urban transportation systems are vulnerable to congestion, accidents, weather, special events, and other costly delays. Whereas typical policy responses prioritize reduction of delays under normal conditions to improve the efficiency of urban road systems, analytic support for investments that improve resilience (defined as system recovery from additional disruptions) is still scarce. In this effort, we represent paved roads as a transportation network by mapping intersections to nodes and road segments between the intersections to links. We built road networks for 40 of the urban areas defined by the U.S. Census Bureau. We developed and calibrated a model to evaluate traffic delays using link loads. The loads may be regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the average annual delay per peak-period auto commuter, and modeled results were found to be close to observed data, with the notable exception of New York City. Resilience was estimated as the change in efficiency resulting from roadway disruptions and was found to vary between cities, with increased delays due to a 5% random loss of road linkages ranging from 9.5% in Los Angeles to 56.0% in San Francisco. The results demonstrate that many urban road systems that operate inefficiently under normal conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should be considered explicitly in roadway project selection and justify investment opportunities related to disaster and other disruptions. [ABSTRACT FROM AUTHOR]

Provision of accurate bus arrival information is vital to passengers for reducing their anxieties and waiting times at bus stop. GPS-equipped buses can be regarded as mobile sensors probing traffic flows on road surfaces. In this paper, we present an approach that predicts bus arrival time in terms of the knowledge learned from a large number of historical bus GPS trajectories. In our approach, we build time-dependent path-section graph, where a path-section is a road segment between two adjacent bus stops, to model the properties of dynamic road networks. Then, a clustering approach is designed to estimate the distribution of travel time on each path-section in different time slots. Finally, bus arrival time is predicted based on the path-section graph and real-time GPS information. Using a real-world trajectory dataset generated by 1000 buses in a period of 2 months, a bus arrival time prediction system is built. Then we evaluate the system with extensive experiments and realistic evaluations. Experiments show that our method is close to the actual value and better than some typical algorithms. [ABSTRACT FROM AUTHOR]

In developing nations, many expanding cities are facing challenges that result from the overwhelming numbers of people and vehicles. Collecting real-time, reliable and precise traffic flow information is crucial for urban traffic management. The main purpose of this paper is to develop an adaptive model that can assess the real-time vehicle counts on urban roads using computer vision technologies. This paper proposes an automatic real-time background update algorithm for vehicle detection and an adaptive pattern for vehicle counting based on the virtual loop and detection line methods. In addition, a new robust detection method is introduced to monitor the real-time traffic congestion state of road section. A prototype system has been developed and installed on an urban road for testing. The results show that the system is robust, with a real-time counting accuracy exceeding 99% in most field scenarios. [ABSTRACT FROM AUTHOR]

In order for the world to stay within the safety threshold of a 2°C increase in average temperature agreed by virtually all governments, the transport sector needs to be decarbonized. The two main obstacles that have prevented this from happening have been the absence of a global legally binding deal and the high relative cost of clean vehicle/energy technologies. The Paris Agreement, which commits countries to reductions of GHG emissions, has virtually solved the first problem and paved the way for countries to implement environmental taxes and subsidies in order to change the relative costs of clean alternatives, which would solve the second problem. These policy actions combined with investment in clean infrastructure and regulation can decarbonize the transport sector. [ABSTRACT FROM AUTHOR]

This paper presents the development of a macroscopic dynamic traffic assignment model for continuum transportation systems with elastic demand. A reactive dynamic user equilibrium model is extended to simulate network equilibrium problems for an urban area with multiple central business districts (CBDs). Each copy of traffic flow reactively makes route choice decisions to minimize the total travel cost from origin to destination, based on instantaneous traffic information from a radio broadcasting service or route guidance system. The elastic demand function for each copy of flow is associated with its total instantaneous travel cost. The model is solved by a cell-centered finite volume method for conservation law equations and a fast sweeping method for Eikonal-type equations on unstructured grids. Numerical experiments for an urban traffic network with two CBDs are presented to verify the rationality of the model and the validity of the numerical method. The numerical results indicate that the model captures some macroscopic characteristics of two copies of flow interacting in time-varying urban transportation systems, e.g., the spatial distributions of the flow density and flux and the path choice behavior in response to elastic demand, and can describe traffic equilibrium phenomena, such as self-organization and traffic congestion build-up and dissipation. [ABSTRACT FROM AUTHOR]