Abstract

This dissertation models and analyzes heterogeneous traffic flow, with a particular focus on mixed traffic flow consisting of cars and two-wheelers. The increase in traffic congestion induces commuters to switch to powered two wheelers (PTWs), i.e. motorcycle, mopeds and scooters, because of their high maneuverability and space efficiency. The growth in number of PTWs, combined with their unique mobility features, results in complex traffic characteristics which are difficult to recreate with the existing modeling approaches. We develop an analytical model that can accurately reproduce the traffic features in a mixed flow of cars and PTWs. The traffic stream is decomposed into two vehicle classes, PTWs and cars. The fundamental properties are derived by employing a porous flow approach. It is assumed that the speed of a vehicle class is dictated by the physical and motion properties of the vehicle class, and the distribution of free spaces on the road. We propose an approximation method to derive the free-space distribution. In order to explore broader aspects of the traffic flow characteristics, notably required by intelligent transport system (ITS) applications, we formulate the model in the Lagrangian and the Eulerian frameworks. Further, we provide a numerical method for the discretization of the mathematical model. We analyze the flow characteristics of mixed PTWs and cars traffic and identify important properties, which give insights for future ITS solutions and traffic policy makers. The applicability of the model for different ITS applications is illustrated. Finally, the developed model is validated using a microsimulation tool.; Cette dissertation modélise et analyse les flux de trafic hétérogènes, avec une attention particulière portée à la circulation de voitures et de deux-roues. L'augmentation du nombre de congestions de trafic a forcé les personnes désirant se déplacer à se diriger vers les le deux-roues (appelé ici PTWs = powered two wheelers), comme les motos, les mopeds et les scooters, du fait de leur facilité de manoeuvre et leur efficacité dans l'espace. L'augmentation du nombre de PTWs combinée au caractère unique de certaines de leurs fonctionnalités a résulté en un trafic complexe, donc les particularités sont difficiles à recréer avec les approches de modélisation existantes. Nous développerons ici un modele analytique permettant de reproduire de manière pertinente les particularités d'un flux de véhicules mêlant à la fois les voitures et les deux-roues. Le trafic se décompose en deux classes de véhicules : les PTWs et les voitures. Les propriétés fondamentales sont déduites en employant une approche "porous flow". On suppose que la vitesse d'un véhicule d’une certaine classe est dictée par les propriétés physiques et motrices du véhicule, ainsi que la distribution d'espace vide sur la route. Nous proposons une méthode d'approximation pour dériver la distribution d'espace vide. Dans le but d'explorer plus largement les caractéristiques du flux de trafic notamment requis par les applications de système de transport intelligent (ITS), nous formulons le modèle dans les cadres lagrangien et eulérien. Puis, nous feront appel à une méthode numérique pour la discrétisation du modèle mathématique. Se basant sur le modèle développé nous analyserons les caractéristiques du flux de trafic pour en identifier les propriétés les plus importantes qui nous permettrons de prédire de futures ITS applications et d'organisations du trafic. La possibilité d'appliquer le modèle pour les différentes ITS applications est illustrée par des exemples. Finalement, le modèle développé est validé à l'aide de l'outil de microsimulation.

Abstract

During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under heterogeneous traffic where on-street bicyclists encounter a complex interaction with various types of vehicles and show divergent operational characteristics. Thus, the present study proposes an initial model suitable for urban road segments in mid-sized cities under such complex situations. For analysis purpose, various operational and physical factors along with user perception data sets (13,624 effective ratings in total) were collected from 74 road segments. Eight important road attributes affecting the bicycle service quality were identified using the most recent and most promising machine learning technique namely, random forest. The identified variables are namely, effective width of outside through lane, pavement condition index, traffic volume, traffic speed, roadside commercial activities, interruptions by unauthorized stoppages of intermittent public transits, vehicular ingress–egress to on-street parking area, and frequency of driveways carrying a high volume of traffic. Service prediction models were developed using ordered probit and ordered logit modeling structures which meet a confidence level of 95%. Prediction performances of developed models were assessed in terms of several statistical parameters and the ordered probit model outperformed the ordered logit model. Incorporating outputs of the probit model, a predictive equation is presented that can identify under what level a segment is offering services for bicycle use. The service levels offered by roadways were classified into six categories varying from ‘excellent’ to ‘worst’ (A–F). Document type: Article

Abstract

"jats:p"Compared to homogeneous traffic flow, traffic speed variation is drastic with the involvement of heterogeneity. With an intent of studying the negative upshot of fluctuating speeds of heterogeneous traffic on the environment, the current paper is the outcome of the research done on various highways located in the states of Andhra Pradesh and Telangana in India, with an objective of developing a comprehensive noise prediction model by taking into account the traffic and roadway factors. Quantified noise levels [Leq (dBA) and L10 (dBA)] revealed that for the traffic speed variation of 10 to 95 kmph, the traffic noise levels were significantly affected by the variations in the proportion of the vehicle. On a specific note, the proposed model can be effectively used for the highway traffic noise prediction especially for the heterogeneous traffic, as the difference between the measured and predicted noise levels are within 1 to 10 dB (A). Document type: Article