Integrated Approach for Diversion Route Performance Management during Incidents

Non-recurrent congestion is one of the critical sources of congestion on the highway. In particular, traffic incidents create congestion in unexpected times and places that travelers do not prepare for. During incidents on freeways, route diversion has been proven to be a useful tactic to mitigate non-recurrent congestion. However, the capacity constraints created by the signals on the alternative routes put limits on the diversion process since the typical time-of-day signal control cannot handle the sudden increase in the traffic on the arterials due to diversion. Thus, there is a need for proactive strategies for the management of the diversion routes performance and for coordinated freeway and arterial (CFA) operation during incidents on the freeway. Proactive strategies provide better opportunities for both the agency and the traveler to make and implement decisions to improve performance. This dissertation develops a methodology for the performance management of diversion routes through integrating freeway and arterials operation during incidents on the freeway. The methodology includes the identification of potential diversion routes for freeway incidents and the generation and implementation of special signal plans under different incident and traffic conditions. The study utilizes machine learning, data analytics, multi-resolution modeling, and multi-objective optimization for this purpose. A data analytic approach based on the long short term memory (LSTM) deep neural network method is used to predict the utilized alternative routes dynamically using incident attributes and traffic status on the freeway and travel time on both the freeway and alternative routes during the incident. Then, a combination of clustering analysis, multi- resolution modeling (MRM), and multi-objective optimization techniques are used to develop and activate special signal plans on the identified alternative routes. The developed methods use data from different sources, including connected vehicle (CV) data and high- resolution controller (HRC) data for congestion patterns identification at the critical intersections on the alternative routes and signal plans generation. The results indicate that implementing signal timing plans to better accommodate the diverted traffic can improve the performance of the diverted traffic without significantly deteriorating other movements\u27 performance at the intersection. The findings show the importance of using data from emerging sources in developing plans to improve the performance of the diversion routes and ensure CFA operation with higher effectiveness

TRAVEL TIME PREDICTION FOR DYNAMIC ROUTING USING ANT BASED CONTROL

Currently most car drivers use static routing devices based on the shortest distance between start and end position. But the shortest route can differ from the shortest route in time. To compute alternative routes it is necessary to have good prediction models of expected congestions and a fast algorithm to compute the shortest path while being able to react to dynamic changes in the network caused by special incidents. In this paper we present a dynamic routing system based on Ant Based Control (ABC). Starting from historical traffic data, ants are used to compute and predict the travel times along the road segments. They are finding the fastest routes not only looking to the past and present traffic conditions but also trying to anticipate and avoid future congestions.

Ereignisorientierte Routenwahl in spontan gestörten Stadtstraßennetzen zur Anwendung eines selbstorganisierten Störfallmanagements

Die Mobilität von Personen und Gütern, insbesondere in Städten, ist der Motor einer Volkswirtschaft. Dieser Motor kommt jedoch ins Stottern, wenn Staubildung im Stadtstraßennetzwerk einsetzt. Eine unvermeidbare Ursache von Staubildung stellen Verkehrsstörfälle dar, die schlimmstenfalls zu Gridlocks führen können. In der Folge werden hohe Kosten für Verkehr, Wirtschaft und Umwelt verursacht. Mit welchen Gegenmaßnahmen kann die Staubildung im Netzwerk effektiv bewältigt werden? Wie können entsprechende Gegenmaßnahmen realistisch noch vor einem praktischen Einsatz bewertet werden? Ausgehend von diesen Fragestellungen, widmet sich diese Dissertation der Entwicklung eines ereignisorientierten Routenwahlmodells für den Stadtstraßenverkehr und eines selbstorganisierten Störfallmanagements als Gegenmaßnahme zur Reduzierung negativer Auswirkungen der Staubildung. Zur Modellierung des Routenwahlverhaltens in ereignisreichen Stadtstraßennetzen wird das ereignisorientierte Routenwahlmodell entwickelt. Der Ausgangspunkt des Modells ist die diskrete Wahltheorie. Entscheidungsprozesse einzelner Autofahrer werden vor und während der Fahrt direkt simuliert. Der Entscheidungsprozess ist dabei maßgeblich von Beobachtungen lokaler Verkehrsbedingungen geprägt. Somit wird nachgebildet, dass Autofahrer flexibel auf unvorhergesehene Ereignisse durch Routenwechsel reagieren können. Auf diese Weise ist eine realistische Simulation des Routenwahlverhaltens von Autofahrern in der Stadt möglich. Das ereignisorientierte Routenwahlmodell ist zudem generisch formuliert. Es lässt sich zur Bewertung von Gegenmaßnahmen für störfallbedingte Staubildung einsetzen und bedient darüber hinaus ein breites Anwendungsspektrum. Der zweite Beitrag dieser Dissertation ist ein selbstorganisiertes Konzept für ein Störfallmanagement in Stadtstraßennetzen als Gegenmaßnahme zur Staubildung. Es vereint zwei lokal wirkende Prinzipien, deren Ausgangspunkte die Lichtsignalanlagen im Stadtnetzwerk sind. Mit verlängerten Rotzeiten werden Fahrzeuge an einer Kreuzung an der Einfahrt in einen Straßenabschnitt gehindert, wenn ein vorgesehener Rückstaubereich ausgeschöpft ist, da andernfalls Blockaden auf den Kreuzungen entstehen. Gleichzeitig werden noch freie Richtungen an der Kreuzung durch verlängerte Grünzeiten attraktiver gestaltet, um Autofahrer zum Umfahren der Staubildung zu motivieren. Die Anwendung der lokalen Wirkungsprinzipien stellt sich vollständig selbstorganisiert, d.h. ohne Vorgabe eines Planers, mit dem Ausmaß der Staubildung im Netzwerk ein. Simulationsstudien in zwei unterschiedlich komplexen Netzwerken haben die Machbarkeit des selbstorganisierten Störfallmanagements nachgewiesen. Gegenüber einem gewöhnlichen Netzwerk konnte für alle untersuchten Störfälle die Akkumulation zusätzlicher Fahrzeuge im Netzwerk während des Störfalls signifikant reduziert werden.The mobility of people and goods, especially in urban areas, is of significant importance for national economies. However, recurrent congestion in urban road networks, caused by increased traffic demand, considerably restrains mobility on a daily basis. Another significant source of congestion are traffic incidents which even might lead to gridlock situations. Congestion raises high costs for traffic, economy and environment. Which countermeasures should be applied for an effective management of urban congestion? How can appropriate countermeasures be realistically evaluated? Based on these questions, this thesis is devoted to the development of an event-oriented route choice model for urban road traffic and a self-organized incident management strategy as an effective countermeasure for urban congestion. The first contribution of this thesis is an event-oriented route choice model for urban road networks. It is based on discrete choice theory and models decision-making processes of individual motorists before and during their journey. A key aspect of the proposed model is the motorist's ability to observe local traffic conditions. These observations are then included in the decision process. In this way, it can be modeled that motorists respond to unforeseen events by route revisions. This allows a realistic simulation of the route choice behavior of motorists in naturally eventful urban road networks. Furthermore, the event-oriented route choice model is flexibly formulated. It can be used for the evaluation of countermeasures for incident-related congestion and, moreover, allows a wide range of applications. The second contribution of this thesis is a self-organized concept of an incident management strategy in urban road networks as a countermeasure for urban congestion. It combines two locally acting principles on the basis of traffic lights in an urban road network. The inflow of vehicles into a road segment is regulated with restricted or skipped green times as soon as an allocated queuing capacity is depleted. Otherwise, blockages would result on the intersection. At the same time, yet free alternative directions are served with regular or even extended green times and, thus, might become more attractive to the driver than the original congested direction. The application of these local principles is realized in a completely self-organized manner, thereby scaling directly with the extent of congestion in the urban road network. Simulation studies in two networks with different complexity have proven the feasibility of the self-organized incident management. Compared to an ordinary network, the extents of additional vehicles due to investigated incidents were significantly reduced

VANET-enabled eco-friendly road characteristics-aware routing for vehicular traffic

There is growing awareness of the dangers of climate change caused by greenhouse gases. In the coming decades this could result in numerous disasters such as heat-waves, flooding and crop failures. A major contributor to the total amount of greenhouse gas emissions is the transport sector, particularly private vehicles. Traffic congestion involving private vehicles also causes a lot of wasted time and stress to commuters. At the same time new wireless technologies such as Vehicular Ad-Hoc Networks (VANETs) are being developed which could allow vehicles to communicate with each other. These could enable a number of innovative schemes to reduce traffic congestion and greenhouse gas emissions. 1) EcoTrec is a VANET-based system which allows vehicles to exchange messages regarding traffic congestion and road conditions, such as roughness and gradient. Each vehicle uses the messages it has received to build a model of nearby roads and the traffic on them. The EcoTrec Algorithm then recommends the most fuel efficient route for the vehicles to follow. 2) Time-Ants is a swarm based algorithm that considers not only the amount of cars in the spatial domain but also the amoumt in the time domain. This allows the system to build a model of the traffic congestion throughout the day. As traffic patterns are broadly similar for weekdays this gives us a good idea of what traffic will be like allowing us to route the vehicles more efficiently using the Time-Ants Algorithm. 3) Electric Vehicle enhanced Dedicated Bus Lanes (E-DBL) proposes allowing electric vehicles onto the bus lanes. Such an approach could allow a reduction in traffic congestion on the regular lanes without greatly impeding the buses. It would also encourage uptake of electric vehicles. 4) A comprehensive survey of issues associated with communication centred traffic management systems was carried out