Abstract

Does consideration of average speed distributions on roads-as compared to single mean speed-lead to different results in emission modelling of large road networks? To address this question, a post-processing method is developed to predict mean speed distributions using available traffic data from a dynamic macroscopic traffic model (Indy) that was run for an actual test network (Amsterdam). Two emission models are compared: a continuous (COPERT IV) and a discrete model (VERSIT+macro). Computations show that total network emissions of CO, HC, NOx, PM10 and CO2 are generally (but not always) increased after application of the mean speed distribution method up to +9%, and even up to +24% at sub-network level (urban, rural, motorway). Conventional computation methods thus appear to produce biased results (underestimation). The magnitude and direction of the effect is a function of emission model (type), shape of the composite emission factor curve and change in the joint distribution of (sub)-network VKT (vehicle kilometres travelled) and speed. Differences between the two emission models in predicted total network emissions are generally larger, which indicates that other issues (e.g., emission model validation, model choice) are more relevant. © 2007 Elsevier Ltd. All rights reserved.

Abstract

This study proposes Reinforcement Learning (RL) based algorithm for finding optimum signal timings in Coordinated Signalized Networks (CSN) for fixed set of link flows. For this purpose, MOdified REinforcement Learning algorithm with TRANSYT-7F (MORELTRANS) model is proposed by way of combining RL algorithm and TRANSYT-7F. The modified RL differs from other RL algorithms since it takes advantage of the best solution obtained from the previous learning episode by generating a sub-environment at each learning episode as the same size of original environment. On the other hand, TRANSYT-7F traffic model is used in order to determine network performance index, namely disutility index. Numerical application is conducted on medium sized coordinated signalized road network. Results indicated that the MORELTRANS produced slightly better results than the GA in signal timing optimization in terms of objective function value while it outperformed than the HC. In order to show the capability of the proposed model for heavy demand condition, two cases in which link flows are increased by 20% and 50% with respect to the base case are considered. It is found that the MORELTRANS is able to reach good solutions for signal timing optimization even if demand became increased. (C) 2015 Elsevier Ltd. All rights reserved.

Abstract

espanolLa necesidad de dar respuesta al freno que suponia la deficitaria red de transporte terrestre para el crecimiento de la economia se convirtio en un objetivo basico de la politica inversora del Estado durante el siglo XX. Un primer esfuerzo constructor se produjo en el primer tercio del siglo, dando inicio a la etapa contemporanea del transporte por carretera en Espana. Tras el paron de la postguerra civil, un nuevo impulso tuvo lugar a partir de 1960 y hasta la crisis de mediados de los setenta, multiplicandose las inversiones destinadas a este fin. Durante la transicion asistimos a una brusca caida de las inversiones que llevo aparejado un estancamiento de la longitud de la red y a la transferencia de una parte de las carreteras a las nacientes Comunidades Autonomas. El pais realizo, desde mediados de los ochenta y ayudado por los fondos europeos, un esfuerzo considerable, pasando de pais atrasado a converger con Europa en equipamiento de grandes infraestructuras de transporte. EnglishThe need to address the constraint on the growth of the Spanish economy caused by the deficient road transport network became a primary objective of the State’s investment policy during the whole of the 20th century. An initial effort to build the network was made in the first thirty years of the century, marking the start of the contemporary era of road transport in Spain. This development was interrupted by the civil war, then, a new boost in road construction took place between 1960 until the crisis of the mid 1970s, a period during which investments in road infrastructure were multiplied. During the Transition period there was a sharp fall in investments, no efforts were made to extend the length of the network and the competences for part of the roads were transferred to the newly-formed Autonomous Regions. From the mid-1980s and with the help of European funds, Spain made a considerable effort to modernise its network. It is no longer underdeveloped and has converged with the rest of Europe in terms of its largescale transport infrastructure facilities. Document type: Part of book or chapter of book

Abstract

Slovakia’s transport sector has not suffered from the dramatic reductions in demand and neglect of its infrastructure that have afflicted many of its neighbors. However, current under-maintenance is eating away at the stock of transport infrastructure and is unsustainable in anything more than the very short term. In addition, despite good intentions, progress on commercializing its transport operations has not progressed far enough to put them in a strong position to confront the pressures they will face when Slovakia enters the European Union, hopefully at the beginning of 2004. There is now a short window of opportunity to make good on deferred maintenance, establish a more sustainable maintenance regime and make good on the previous good intentions for commercialization. The opportunity exists also for the institutional structure of the sector to be revised so as to better reflect the interests of transport users, reform the way that infrastructure is financed and to systematically eliminate the remaining regulatory protections given to existing operators. The Strategy presented here shows how these objectives can best be reached, and how the World Bank can help bring them about. If the Strategy is implemented, action will have been taken before there is a problem and order will have been introduced before disorder takes over.

Abstract

This paper presents a comparative analysis of the level-of-service (LOS) determination based on the VISSIM simulation software and technique described in Highway Capacity Manual (HCM). The methodology used in the analysis is that of the level of service determination for road transport network as exemplified by T-shape intersection and partial cloverleaf (parclo AB3) interchange in the city of Kryvyi Rih. The Department of Road Safety of the National Patrol Police of Ukraine set the task to determine the vehicles queuing length on the bridge if traffic lights at the T-shape intersection are installed. To conduct the project, the VISSIM simulation model of the intersection was developed according to site data collection on workdays and at weekends. LOS criteria were determined by using both traffic management assessment methods. The scenario of traffic lights installation at the intersection was elaborated. It was determined that with traffic signalization adjustment the average travel speed would be about 40% of the free flow speed for a street class. The two assessment methods vary significantly regarding traffic flow density criterion (35.1%). The conclusion is that flow density criterion is not reliable for the node LOS definition. By way of application value of the research findings the recommendations have been prepared for T-shape intersection traffic management that dismissing the project of traffic lights installation at the intersection needs to be considered.

Abstract

In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting, reduce the requirements of computational resources, and be utilized effectively by various methodologies and models. Inspired by pooling operations in deep learning, we propose a representation framework for traffic congestion data in urban road traffic networks. This framework consists of grid-based partition of urban road traffic networks and a pooling operation to reduce multiple values into an aggregated one. We also propose using a pooling operation to calculate the maximum value in each grid (MAV). Raw snapshots of traffic congestion maps are transformed and represented as a series of matrices which are used as inputs to a spatiotemporal congestion prediction network (STCN) to evaluate the effectiveness of representation when predicting traffic congestion. STCN combines convolutional neural networks (CNNs) and long short-term memory neural network (LSTMs) for their spatiotemporal capability. CNNs can extract spatial features and dependencies of traffic congestion between roads, and LSTMs can learn their temporal evolution patterns and correlations. An empirical experiment on an urban road traffic network shows that when incorporated into our proposed representation framework, MAV outperforms other pooling operations in the effectiveness of the representation of traffic congestion data for traffic congestion prediction, and that the framework is cost-efficient in terms of computational resources. Document type: Article

Abstract

dverse weather conditions regularly lead to severe congestion and large travel time delays on road networks all over the world. Different climate scenarios indicate that in the future adverse weather conditions are likely to become more frequent, last longer and will be more extreme. Although climate mitigation measures are being taken, it remains important to investigate how adverse weather events will affect the performance of the road network in the future. The main objective of this paper is to give an overview of how the impact of adverse weather conditions and adaptation measures on road network performance can be quantified. A literature review has been performed to show what is empirically known about the impact of adverse weather conditions on the road network performance. Furthermore, available methods to quantify the impact of adverse weather conditions and adaptation measures on the road network performance for future situations are reviewed. As an example, a case study for the municipality of Rotterdam has been carried out that shows how a combination of models can be used to analyse which links in the road network are most vulnerable for increasingly severe local weather related disturbances. The results of the case study allow local authorities to decide whether or not they need to take adaptation measures. © 2016, Editorial Board EJTIR. All rights reserved.

Abstract

The quality aspects of OpenStreetMap (OSM), as the global representation of crowd-sourced mapping, have always been of priomary concern to academics. While the methodologies for checking its quality against the national maps have been implemented by a number of studies, there are minimal works on how to practically improve the quality of OSM towards being an authoritative map source. This paper presents a method for conflating road attributes, namely the name and reference code, of OSM with the Open Data provided by Ordnance Survey (the British national mapping agency). The added values in the proposed methodology include the daily updates and serving of the conflated maps via open Web Services. More importantly, the OSM crowd correction is facilitated by frequently highlighting and web-serving the individual differences. There are currently over 5,800 differences in matching road names and references between the two datasets. In addition to describing the conflation methodology, the different geographic distribution patterns of the identified differences are discussed. A negative effect of the road density on the ratio of the mismatched features between the two datasets is observable, evidenced by their different geographical distribution over the map. It is shown that the best correspondence between attributes exists in the very dense areas, followed by the very low density areas, and lastly in the middle to large sized cities. Document type: Article