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Expanding road networks to accommodate various activities has significantly increased urban noise pollution, adversely affecting human health and quality of life. Numerous factors influence the noise level in urban areas, including road characteristics, traffic characteristics, surrounding buildings, and weather conditions. While previous studies have considered many of these factors individually, this study aims to integrate all relevant variables to comprehensively monitor and analyze their combined effects on noise levels. The objective is to determine the most influential factors that could be incorporated into effective noise reduction strategies. This research focuses on Cairo, one of the most densely populated cities in the world, where high noise levels are a persistent issue. A detailed case study of Tahrir Street in Dokki, Cairo, provides the basis for this investigation. One of the most crowded areas is El-Tahrir Street in Al-Dokki, which was selected as a case study in this research. This area experiences high traffic volume, with up to 1700 vehicles passing through within a 15-min interval. This significant traffic volume is the primary driver of the elevated noise levels in the area. Traffic and noise level monitoring was conducted using a field survey using the sound level meter. Consequently, a statistical analysis was conducted to investigate the correlation between different factors and the noise level and determine the most influential factors. The study revealed that traffic volume and congestion are the most significant factors influencing noise levels on Tahrir Street, exhibiting strong positive correlations (R = 0.38). Additionally, the study found an inverse relationship between vehicle speed and noise level due to high traffic volumes and identified that building characteristics and wind direction also play roles, albeit to a lesser extent. [ABSTRACT FROM AUTHOR]

We discuss a widely used air traffic flow management formulation. We show that this formulation can lead to a solution where air delays are assigned to flights during their take-off which is prohibited in practice. Although air delay is more expensive than ground delay, the model may assign air delay to a few flights during their take-off to save more on not having as much ground delay. We present a modified formulation and verify its functionality in avoiding incorrect solutions. [ABSTRACT FROM AUTHOR]

The use of the Internet of things (IoT) devices is growing rapidly in the current industrial market. Integrating these devices into the network system has subjected the information to Cybersecurity attacks putting the market of IoT technology at risk. As a result, accurate protection algorithms and efficient monitoring systems need to be developed. This paper proposes a new framework design relays on a stochastic measurement of determining parameters based on a novel adaptive deep learning (ADL) algorithm for industrial manufacturing such as transportation, energy. The new framework will be designed to incorporate an element of randomness as opposed to deterministic frameworks. The implemented method considered the network forensic systems and intrusion detection (ID); therefore, the prediction model is set up to alert the administrator about the system status. The framework is proposed based on five prioritized protection stages. The developed model tested on the Bot-IoT dataset, which is well-structured and valid for training, as confirmed by literature. The result has shown the ability to simulate the IoT network traffic along with risk assessment against different types of attacks by using the ADL algorithm. The new framework will be designed to improve computational efficiency by being able to adjust their structure based on the inputs. The research contributions are that it will help in the development of an algorithm which can minimize traffic accidents, improve traffic flow, minimize energy wastes, and save costs of controlling traffic flow. Findings show that using this new algorithm has 100% accuracy, which makes it an excellent choice for the detection of threats and errors. [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]

Snowfall presents a hazard to drivers by reducing visibility and increasing safe stopping distances. Some drivers cancel trips if snowfall is occurring or forecast, and traffic volumes often decrease on snowy days. Lake-effect snow is very localized and is thus hypothesized to have a lesser influence on traffic volume than synoptic-scale snow, which usually covers a broader areal extent. We analyze traffic volume in northeast Ohio during 25 snow events and use a matched-pair analysis to determine whether volumes differ between lake-effect and synoptic-scale snowfall in these regions. We also examine the rate at which traffic volume decreases during snow events by time of day and day of week. Results indicate that there is little difference in mean traffic volume decreases when comparing lake-effect and synoptic-scale snow. Hourly trends suggest that traffic volume is most sensitive to snowfall during the midday on weekdays and late afternoon on weekends and least sensitive to snowfall during the overnight hours. Findings presented herein can assist in transportation planning, risk analysis and roadway safety. [ABSTRACT FROM AUTHOR]

A promising methodology is proposed to estimate reliable annual average daily traffic (AADT) volumes for no-surveyed road sections using probe volumes collected by a vehicle global positioning system (GPS). This research was inspired by the obvious concept that probe counts are a direct portion of AADT from the viewpoint of vehicle trip behavior. The method converts the probe volume of target road section to AADT using the nonlinear relationship between geographical neighborhoods composed of observed AADT volumes and annual average daily probe volumes. The relationship is determined with a locally weighted power-curve model. A feasibility of the proposed method was demonstrated through a case study using real-world data. Analysis results show that the proposed method is a practical and cost-effective way to estimate reliable AADT for unmeasured road segments. This indicates that there exists a strong relationship between AADT values and vehicle-GPS probe values from the trip characteristics of a road network. [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]

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]

Recently, automation, shared use, and electrification are viewed as the “three revolutions” in the future transportation sector, and the traditional scheduled public transit system will be greatly enhanced with flexible services and autonomous vehicle scheduling capabilities. Many emerging scheduled transportation applications include the fully automatic operation system in urban rail transit, joint line planning, and timetabling for high-speed rail as well as emerging self-driving vehicle dispatching. The vehicle routing problem (VRP) holds promise for seeking an optimal set of vehicle routes and schedules to meet customers’ requirements and plays a vital role in optimizing services for feature scheduled transportation systems. Due to the difficulty of finding optimal solutions for large-scale instances, enormous research efforts have been dedicated to developing efficient algorithms, while our paper presents a unique perspective based on a time-dependent and state-dependent path searching framework. An open-source and light-weight VRP with pickup and delivery with time windows (VRPPDTW) modeling package, namely VRPLite, has been developed in this research to provide a high-quality and computationally efficient solution engine for transportation on demand applications. This paper describes the space-time-state modeling process of VRPPDTW using a hyper-network representation. This solution framework can be embedded in a column generation or Lagrangian relaxation framework to handle many general applications. A number of illustrated examples are presented to demonstrate the effectiveness of the path search algorithm under various traffic conditions and passenger travel requirements. [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]

Background: Tractors and other slow-moving self-propelled farm equipment are often used on public roadway to transfer goods from the farm to a market or distributer. Increased roadway exposure has led to a growing concern on the occurrence of farm equipment crashes. This study aims to compare characteristics of road segments with farm equipment crashes to road segments without farm equipment crashes in the state of Iowa. Methods: Data were obtained from the Iowa Department of Transportation from 2005 to 2011 on all crashes involving farm equipment, and features of all Iowa roadways. Geographic Information Systems (GIS) was used to identify geospatial features, such as road type, speed limit, traffic volume surface type, road and shoulder width of where a crash occurred. Logistic regression models were used to measure the associations between road characteristics and the occurrence of farm equipment crashes. Crude and adjusted odds ratios and 95% confidence intervals were reported. Results: A total of 1371 farm equipment crashes were reported in Iowa over the 6-year period and geocoded onto a street location. As traffic volume increased, the odds of a crash occurring also increased. Roadways with posted speed limits between 50 and 60 mph were associated with a higher odds of having crashes on them compared to roadways with speeds less than 35 mph (OR = 8.05, 95% CI: 6.59-9.84). Iowa routes (OR = 5.98, 95% CI: 4.97-7.20) had the highest odds of having crashes compared to local routes. Increased road width (OR = 0.90, 95% CI: 0.86-0.94) was associated with a 10% decrease in the odds of a crash. Conclusions: Higher traffic volume, higher posted speed limits, road type, and smaller road widths were associated with the occurrence of farm equipment crashes. Findings from this study can be used to guide policy to improve roadway design and conditions for all road users. [ABSTRACT FROM AUTHOR]

The vulnerability to real-life networks against small initial attacks has been one of outstanding challenges in the study of interrelated networks. We study cascading failures in two interrelated networks S and B composed from dependency chains and connectivity links respectively. This work proposes a realistic model for cascading failures based on the redistribution of traffic flow. We study the Barabási-Albert networks (BA) and Erdős-Rényi graphs (ER) with such structure, and found that the efficiency sharply decreases with increasing percentages of the dependency nodes for removing a node randomly. Furthermore, we study the robustness of interrelated traffic networks, especially the subway and bus network in Beijing. By analyzing different attacking strategies, we uncover that the efficiency of the city traffic system has a non-equilibrium phase transition at low capacity of the networks. This explains why the pressure of the traffic overload is relaxed by singly increasing the number of small buses during rush hours. We also found that the increment of some buses may release traffic jam caused by removing a node of the bus network randomly if the damage is limited. However, the efficiencies to transfer people flow will sharper increase when the capacity of the subway network αS > α0. [ABSTRACT FROM AUTHOR]

Vehicular Ad-Hoc Network (VANET) is an application of Ad-Hoc Network, which can significantly improve the efficiency of transportation systems. The authentication of information is particularly important in the VANET system, because of its significant impact, and the transportation systems may be paralyzed as a result of receiving the wrong traffic information. Hence, a lot of schemes have been proposed to verify the information of VANET. However, most of currently known schemes verify the information on a one by one basis. In real situation, the large amount of traffic flow will generate a lot of information at the same time. If the authentication method is authenticating one by one, it is bound to lead to information delays, and the system will have difficulty to achieve real-time performance. Therefore, we shall propose an improved authentication of the batch scheme based on bilinear pairing to make VANET more secure, efficient, and more suitable for practical use. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of management, maintenance and planning of interventions in transport networks located in seismic zones, in relation to the actual state of degradation of their most vulnerable elements, as bridges. The study consists in two phases: the first phase is concerned with definition of the seismic vulnerability of a typical bridge in the network, through the construction of fragility curves calculated taking into account the corrosion of the reinforcing steel as the main cause of environmental deterioration. Once the fragility curves of the deteriorated bridges are computed, the second phase consists in the analysis of the vulnerability of the transport network in which the bridges are included taking into account the modification of the traffic flows when bridge infrastructures are damaged. The results of this pilot study can be used as a first step for a proper allocation of economic resources in the planning of seismic retrofit interventions to minimize the overall risk and manage the immediate post-earthquake emergency phase and guide rescuers in reaching the affected and critical areas. [ABSTRACT FROM AUTHOR]

This paper reviews the progress that has been made over the last half-century in modeling and analyzing the growth of transportation networks. An overview of studies has been provided following five main streams: network growth in transport geography; traffic flow, transportation planning, and network growth; statistical analyses of network growth; economics of network growth; and network science. In recognition of the vast advances through decades in terms of exploring underlying growth mechanisms and developing effective network growth models, the authors also point out the challenges that are faced to model the complex process of transport development. [ABSTRACT FROM AUTHOR]

The efficiency of traffic flows in urban areas is known to crucially depend on signal operation. Here, elements of signal control are discussed, based on the minimization of overall travel times or vehicle queues. Interestingly, we find different operation regimes, some of which involve a “slower-is-faster effect”, where a delayed switching reduces the average travel times. These operation regimes characterize different ways of organizing traffic flows in urban road networks. Besides the optimize-one-phase approach, we discuss the procedure and advantages of optimizing multiple phases as well. To improve the service of vehicle platoons and support the self-organization of “green waves”, it is proposed to consider the price of stopping newly arriving vehicles. [ABSTRACT FROM AUTHOR]

Despite the importance of urban traffic flows, there are only a few theoretical approaches to determine fundamental relationships between macroscopic traffic variables such as the traffic density, the utilization, the average velocity, and the travel time. In the past, empirical measurements have primarily been described by fit curves. Here, we derive expected fundamental relationships from a model of traffic flows at intersections, which suggest that the recently measured fundamental diagrams for urban flows can be systematically understood. In particular, this allows one to derive the average travel time and the average vehicle speed as a function of the utilization and/or the average number of delayed vehicles. [ABSTRACT FROM AUTHOR]

We study the duration of “high-flow states” in freeway traffic, defined as the time periods for which traffic flows exceed a given flow threshold. Our empirical data are surprisingly well represented by a power law. Moreover, the power law exponent for a two-lane freeway seems to be independent of the chosen flow threshold. In order to interpret this discovery, we investigate a simple theoretical model of heterogeneous traffic with overtaking maneuvers, which is able to reproduce both, the empirical power law and its exponent. [ABSTRACT FROM AUTHOR]

The U.S. airline network is one of the most advanced transportation infrastructures in the world. It is a complex geospatial structure that sustains a variety of dynamics including commercial, public, and military operations and services. We study the U.S. domestic intercity passenger air transportation network using a weighted complex network methodology, in which vertices represent cities and edges represent intercity airline connections weighted by average daily passenger traffic, non-stop distance, and average one-way fares. We find that U.S. intercity passenger air transportation network is a small-world network accompanied by dissortative mixing patterns and rich-club phenomenon, implying that large degree cities (or hub cities) tend to form high traffic volume interconnections among each other and large degree cities tend to link to a large number of small degree cities. The interhub air connections tend to form interconnected triplets with high traffic volumes, long non-stop distances, and low average one-way fares. The structure of the U.S. airline network reflects the dynamic integration of pre-existing urban and national transportation infrastructure with the competitive business strategies of commercial airlines. In this paper we apply an emerging methodology to representing, analyzing, and modeling the complex interactions associated with the physical and human elements of the important U.S. national air transport and services infrastructure. [ABSTRACT FROM AUTHOR]

The need for acquiring the current-year traffic data is a problem for transport planners since such data may not be available for on-going transport studies. A method is proposed in this paper to predict hourly traffic flows up to and into the near future, using historical data collected from the Hong Kong Annual Traffic Census (ATC). Two parametric and two non-parametric models have been employed and evaluated in this study. The results show that the non-parametric models (Non-Parametric Regression (NPR) and Gaussian Maximum Likelihood (GML)) were more promising for predicting hourly traffic flows at the selected ATC station. Further analysis encompassing 87 ATC stations revealed that the NPR is likely to react to unexpected changes more effectively than the GML method, while the GML model performs better under steady traffic flows. Taking into consideration the dynamic nature of the common traffic patterns in Hong Kong and the advantages/disadvantages of the various models, the NPR model is recommended for predicting the hourly traffic flows in that region. [ABSTRACT FROM AUTHOR]

Improving train traffic control can be a cost-efficient way to improve train traffic punctuality and increase utilization of existing and future railway infrastructure. However, performance in train traffic control tasks currently involves working on a technical level in order to regulate the traffic flow. Working in a preventive manner is poorly supported and train traffic controllers are usually restricted to just solving problems as they occur. This often results in unnecessarily long delays and decreased timeliness of train traffic. The main objective of this paper is to describe a proposed control strategy and a case study, which evaluates the control strategy and the prototype tool derived from the research. By shifting the control paradigm to a high-level control strategy, many of today’s problems may be avoided, with benefits of the reduction in delays, improved timeliness and better utilization of the infrastructure. Twenty-one train traffic controllers participated in a case study, with a simulated prototype environment. The majority of the participating train traffic controllers were positive to the new concepts and ideas. Many of the important aspects of the proposed control strategy can be investigated with the simulation, but due to the complexity of train traffic some issues must be evaluated in an operative environment. [ABSTRACT FROM AUTHOR]

The objective of this paper is to introduce the innovative transport policies of Seoul that have recently been planned and implemented. In this paper, the various transport policies of Seoul are classified and explained according to two important paradoxes that are highlighted by Downs and Thomson, and Edgeworth. These paradoxes describe how to break the vicious circle of car and public transport use by restraining car use and encouraging the use of public transport, and thus indicate how traffic congestion problems might be solved. [ABSTRACT FROM AUTHOR]

The distribution of freight is a major contributor to the levels of traffic congestion in cities. However it is much neglected in the research and planning activities of government, where the focus is disproportionately on passenger vehicle movements. Despite the recent recognition of the contribution of freight transportation to the performance of urban areas under the rubric of city logistics, we see a void in the study of how the stakeholders in the supply chain might cooperate through participation in distribution networks, to reduce the costs associated with traffic congestion. Given that transport costs are typically over 45 of all distribution costs, with congestion a major contributor in the urban setting, the importance of establishing ways in which supply chain partnerships might cooperate to reduce levels of freight vehicle movements has much merit. This paper sets out a framework to investigate how agents in a retail supply chain might interact more effectively to reduce the costs of urban freight distribution. We propose an interactive agency choice method as a way of formalising a framework for studying the preferences of participants in the supply chain to support specific policy initiatives. Such a framework is a powerful way of investigating the behavioural response of each agent to many policies, including congestion pricing, as a way of improving the efficient flow of traffic in cities. [ABSTRACT FROM AUTHOR]

In a standard transportation network, if two nodes are adjacent (directly connected), with flow on the arc between them, then the difference in equilibrium prices at the nodes is explained by the arc’s marginal cost of use, marginal losses, and congestion charge (if the arc is used to its capacity). This paper develops, for electric transmission networks, a relationship between the hourly spot price at a node, and the prices at all nodes adjacent to it: a weighted average of the separate relationships that would exist between the node’s price and each of its adjacent nodes if the network were a standard one. [ABSTRACT FROM AUTHOR]

Main methods and concepts of mathematical modeling of traffic flows were reviewed. Two important lines of research—modeling of loading of urban transportation network and modeling of traffic flow dynamics—were discussed. For modeling of loading, consideration was given to the models for calculation of correspondences and distribution of flows over the network including different variants of equilibrium distribution and the optimal strategy algorithm. The main classes of dynamic models—macroscopic (hydrodynamic), kinetic, and microscopic—were examined as well. [ABSTRACT FROM AUTHOR]

An extensive road system with rapidly increasing traffic produces diverse ecological effects that cover a large land area. Our objective was to evaluate the effect of roads with different traffic volumes on surrounding avian distributions, and its importance relative to other variables. Grassland bird data (5 years) for 84 open patches in an outer suburban/rural landscape near Boston were analyzed relative to: distance from roads with 3000–8000 to >30,000 vehicles/day; open-habitat patch size; area of quality microhabitat within a patch; adjacent land use; and distance to other open patches. Grassland bird presence and regular breeding correlated significantly with both distance from road and habitat patch size. Distance to nearest other open patch, irrespective of size, was not significant. Similarly, except for one species, adjacent land use, in this case built area, was not significant. A light traffic volume of 3000–8000 vehicles/day (local collector street here) had no significant effect on grassland bird distribution. For moderate traffic of 8000–15,000 (through street), there was no effect on bird presence although regular breeding was reduced for 400 m from a road. For heavier traffic of 15,000–30,000 (two-lane highway), both bird presence and breeding were decreased for 700 m. For a heavy traffic volume of ≥30,000 vehicles/day (multilane highway), bird presence and breeding were reduced for 1200 m from a road. The results suggest that avian studies and long-term surveys near busy roads may be strongly affected by traffic volume or changes in volume. We conclude that road ecology, especially the effects extending outward >100 m from roads with traffic, is a sine qua non for effective land-use and transportation policy. [ABSTRACT FROM AUTHOR]

This research investigates freeway-flow impacts of different traveler types by specifying and applying a latent-segmentation model of congested and uncongested driving behaviors. Drivers in uncongested conditions are assumed to drive at self-chosen speeds, while drivers in congested conditions are assumed to take speed as given and choose a spacing (between their vehicle and the previous vehicle). Several classes of driver-vehicle combinations are distinguished in a data set based on double-loop-detector pulses and a household travel survey. These classifications are made on the basis of vehicle type and gender, leading to class estimates of speeds and spacings. The segmentation model is specified as a logit function of density, weather, and vehicle type, leading to estimates of congested-condition probabilities. Unobserved heterogeneity is incorporated in all models via common error assumptions. Results indicate that segmentation models are promising tools for traffic data analysis and that information on travelers, their vehicles, and weather conditions explains significant variation in flow data. By clarifying a greater understanding of traffic conditions and traveler behavior explains much scatter in the fundamental relation between flow, speed, and density, can assist regions in their traffic-management efforts and engineers in their design of roadway facilities. Ultimately, such improvements to travel networks should enhance quality of life. [ABSTRACT FROM AUTHOR]

This paper shows that monitoring campaigns in association with modelling, particularly detailed roadside modelling, are a useful and cost effective method of assessing air quality. It proposes that an approach based on detailed monitoring associated with concurrent meteorological data can be used in combination with a roadside model to assess concentrations at other locations near a road and in other weather conditions. Plans for improving air quality can also be assessed in simple ways. The need for complex models based on detailed emission inventories is avoided. Two examples from streets in London and Paris are presented in which measurements of CO, NOx and volatile organic compounds have been made. The measurements are interpreted using correlations between pollutant concentrations and with wind speed, and by using the USEPA CALINE4 model. It is demonstrated that air quality measurements can be used to assess the validity of road traffic emission factors. [ABSTRACT FROM AUTHOR]

An analytical model for the determination of the number and locations of time points as well as the amount of slack times in transit schedule design is developed. The model considers a bus route with a special passenger demand pattern in which all boarding passengers coordinate their arrivals at each stop in such a way that they never miss their intended bus, and therefore designing the schedule separately, a single run at a time, becomes possible. The model employs the dynamic programming method to deal with the trade-offs among various cost components associated with the schedule quantitatively, and yet is flexible enough to incorporate the existing rules of thumb as well as transit operators' policies. Numerical examples that illustrate the applications of the model are given. The model, although not quite applicable to bus routes with general passenger demand patterns, is useful in the analysis of the contributing factors to the design of an economical, reliable, and operational transit schedule, and is likely to be adaptable for more realistic cases. [ABSTRACT FROM AUTHOR]

A basic issue in the planning of urban public transport is the determination of headways or inter-dispatch times. During each season, i.e. distinct time-period whose demand characteristics are constant, the following tradeoff must be considered. Dispatching too many vehicles on a route causes high operating costs, while too few vehicles may result in unsatisfactory levels of service. An appropriate policy on headways will help to balance resources between lines (routes) in peak-demand hours and will influence the total number of buses acquired by a transit company. Previous practice in industry usually bases the planning of headways upon satisfying service criteria on a ‘most-congested segment’. This approach reduces the problem from that of studying a route to that of a single segment (stop), but thereby fails to account for other important information about the line's characteristics. In this article, we develop two new service criteria which consider the line as a whole: (1) ‘crowding-overdistance’ takes into account discomfort resulting from a vehicle carrying too many passengers, and the corresponding distance travelled; and (2) ‘probability-of-failure’, the frequency with which a waiting passenger fails to board due to lack of space. COD will be analyzed using simulation. POF will be related to a time-dependent Markov chain that is ‘inhomogeneous’ in terms of distance along the route. Optimal headways are those which dispatch the smallest number of buses while meeting the particular service criterion. Models based on each of the two criteria are illustrated and applied to a number of routes of the Israeli transit company, DAN. [ABSTRACT FROM AUTHOR]

Short term traffic forecasting is one of the important fields of study in the transportation domain. Short term traffic forecasting is very useful to develop a more advanced transportation system to control traffic signals and avoid congestions. Several studies have made efforts for short term traffic flow forecasting for divided and undivided highways across the world. However, all these studies relied on the dataset which are greatly varied between countries due to the technology used for transportation data collection. India is a developing country in which efforts are being done to improve the transportation system to avoid congestion and travel time. Two-lane undivided highways with mixed traffic constitute a large portion of Indian road network. This study is an attempt to develop a short term traffic forecasting model using back propagation artificial neural network for two lane undivided highway with mixed traffic conditions in India. The results were compared with random forest, support vector machine, k-nearest neighbor classifier, regression tree and multiple regression models. It was found that back-propagation neural network performs better than other approaches and achieved an R2 value 0.9962, which is a good score. [ABSTRACT FROM AUTHOR]

Purpose: A traffic safety analysis that is based on registered crashes often suffers from underreporting, which may result in biased conclusions and lead to misguided crash-prevention strategies. Self-reporting traffic crashes is a complementary method to obtain crash information that is often not available in official databases. By surveying studies from around the world, this paper aims to map the current practices in the collection of data from self-reporting traffic crashes.Method: A systematic literature search was carried out in three databases, ScienceDirect, Scopus and Transport Research International Documentation (TRID), resulting in 134 reviewed studies.Results: Self-reported crash studies were found to be more common in Europe, North America and Australasia, but there are few studies in developing countries. The reviewed studies mostly focused on adult road users (i.e. legal age of obtaining driving license and with no upper limit) and car users. Questionnaires (either paper based or online) were the most often used method, and 1 year was the most common recall period used. Regardless of its drawbacks, the reviewed studies showed that researchers ‘trust’ self-reports.Conclusion: More studies should be conducted, especially targeting adolescent and young adults (age of 15-30 years) and vulnerable road users (VRUs). Developing countries should increase their efforts when it comes to using self-reporting to better assess the actual traffic safety situation and produce knowledge-based appropriate safety measures. Utilisation of smartphone application to assist data collection in self-reporting study for in-depth crash analysis should be explored further. [ABSTRACT FROM AUTHOR]

Background: Understanding traffic is an important challenge in different scientific fields. While there are many approaches to constructing traffic models, most of them rely on origin-destination data and have difficulties when phenomena should be investigated that have an effect on the origin-destination matrix.Methods: A macroscopic traffic model is introduced that is novel in the sense that no origin-destination data are required as an input. This information is generated from mobility behavior data using a hybrid approach between agent-based modeling to find the origin and destination points of each vehicle and network techniques to find efficiently the routes most likely used to connect those points. The simulated road utilization and resulting congestion is compared to traffic data to quantitatively evaluate the results. Traffic jam avoidance behavior is included in the model in several variants, which are then all evaluated quantitatively.Results: The described model is applied to the City of Graz, a typical European city with about 320,000 inhabitants. Calculated results correspond well with reality.Conclusions: The introduced traffic model, which uses mobility data instead of origin-destination data as input, was successfully applied and offers unique advantages compared to traditional models: Mobility behavior data are valid for different systems, while origin-destination data are very specific to the region in question and more difficult to obtain. In addition, different scenarios (increased population, more use of public transport, etc.) can be evaluated and compared quickly. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including urban sustainability, transportation and traffic flow.