Abstract

Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies in Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper. © Association for Scientific Research. Document type: Article

Abstract

Modeling vehicle delay has been an interesting subject for traffic engineers and urban planners. Determination of vehicle delay is a complex task and the delay is influenced by many variables that have uncertainties and vagueness, especially for non-uniform or over-saturated conditions. In this study, vehicle delay is modeled using new approaches such as Fuzzy Logic (FL) and Artificial Neural Networks (ANN) to deal with all conditions. The Neuro Fuzzy Delay Estimation (NFDE) model and Artificial Neural Networks Delay Estimation (ANNDE) model are developed. The overall delay data required for the model were collected from ten signalized intersections in Turkey. The results of the developed models are compared with the Highway Capacity Manual (HCM), Akqelik's methods and the delay data collected from intersections. The results showed that delay estimations by the ANNDE and NFDE model are promising. It is also inferred that the NFDE model results are the best fitted. The Average Relative Error (ARE) rates of NFDE model are determined as 7% for under-saturated and 5% for over-saturated conditions. The results reflect the fact that the neuro-fuzzy approach may be used as a promising method in vehicle delay estimation. (c) 2006 Elsevier Ltd. All rights reserved. C1 Pamukkale Univ, Fac Engn, Dept Civil Engn, TR-20070 Kinikli, Denizli, Turkey.

Abstract

n 80 V bipolar lead-acid battery was constructed and tested using hybrid electric vehicle (HEV) drive cycles. Drive cycles with a peak power of 6.7 kW, equal to 1/5 of the total power profile required for the HEV studied, were run successfully. Model calculations showed that the 80 V module constructed, which is at the moment 2.5 times heavier than required for the HEV operation studied, can be optimized to meet the requirements.

Abstract

Transport energy modeling is a subject of current interest among transport engineers and scientists concerned with problems of sustainable transport. Transport energy planning is not possible without a reasonable knowledge of past and present energy consumption and likely future demands. In this study, three forms of the energy demand equations are developed in order to improve transport energy demand estimation efficiency for future projections based on genetic algorithm (GA) notion. The Genetic Algorithm Transport Energy Demand Estimation (GATEDE) model is developed using population, gross domestic product and vehicle-km. All equations proposed here are linear and non-linear, of which one is linear, second is exponential and third is quadratic. The quadratic form of the GATEDE model provided better-fit solution to the observed data and can be used with a high correlation coefficient for Turkey's future transport energy projections. It is expected that this study will be helpful in developing highly applicable and productive planning for transport energy policies. The GATEDE gives transport energy demand in comparison with the other transport energy demand projections. The GATEDE model plans the sectoral energy demand of Turkey until 2020. (C) 2003 Elsevier Ltd. All rights reserved. C1 Pamukkale Univ, Fac Engn, Dept Civil Engn, PAU Insaat Muh Kinikli, TR-20017 Denizli, Turkey.

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

The transport of students presents important challenges in the case of the city of Bogota, where an important cluster of schools is located in one zone, but there is only one road connecting these schools to residential zones. Thus, traffic congestion is high, generating long travel times for students, high operational costs, and mobility problems. This paper studies the impacts of a cooperative strategy between logistics operators using a mixed integer programming mathematical model, to find the optimal design of school routes on a network with the topology that describes the aforementioned road system. Two strategies are compared: a mixed loads strategy, where students from different schools share buses; and a single load strategy, where students from different schools cannot share buses. The objective is to minimize the total operational costs while satisfying the schools’ time windows. Comparative results of the two models using exact and heuristic approaches are presented. Resumen El transporte de estudiantes tiene desafíos importantes en el caso de la ciudad de Bogotá, donde un grupo de escuelas se encuentra en una zona, pero sólo hay una carretera que las conecta con zonas residenciales. Por lo tanto, la congestión del tráfico es alta, generando largos tiempos de viaje, altos costos de operación y problemas de movilidad. Se estudia el impacto de una estrategia cooperativa entre operadores logísticos a través de modelos de programación de entera mixta, para encontrar el diseño óptimo de rutas escolares en una red con la topología que describe el mencionado sistema vial. Se comparan dos estrategias: Cargas mixtas y carga única, donde los estudiantes de diferentes escuelas comparten o no los autobuses disponibles. El objetivo es minimizar los costos totales de operación respetando las ventanas de tiempo de las escuelas. Se presentan los resultados comparativos de los modelos usando enfoques exactos y heurísticos. Document type: Article

Abstract

Ths skid resistance of the road surface is one of the parameters that influence the active vehicle safety and consequently the traffic safety. Up till now, in the Netherlands skid resistance has been evaluated by means of of a statistical relation between the skid resistance value and accident risk. In order to be able to fulfil the demands of a more detailed evaluation an approach is suggested with the focus on the physical description of mechanisms in traffic safety. This approach is innovative and quite different from standard solutions in traffic safety research. One of the major challenges in this approach is to integrate a wide range of disciplines, i.e. road technology, tyre-road interaction, vehicle dynamics and human factors for the prediction of the braking performance of vehicle from specified road characteristics. In this way, the braking performance model supports a better mutual understanding between the governments, road constructors and vehicle manufacturers. One of the crucial elements in the model is the accurate description of the tyre-road interaction. The Magic Formula tyre model is a general accepted model to describe interaction forces within the automotive industry. This paper focuses on the extension of the Magic Formula tyre model with speed dependent friction for especially the wet conditions and also the scaling of the Magic Formula parameters based on standard road-skid resistance and road macro texture measurements. This extended model is valid for dry and wet surfaces. It enables the scientist to enlarge the accuracy and validity range of the tyre-road interaction model towards a wide range of surfaces and thus increasing the accuracy of the prediction of the vehicle braking performance