The early observation and elimination of non-recurring incidents is a crucial task in trafficmanagement. The performance of the conventional incident detection methods (trafficcameras and other sensory technologies) is limited and there are still challenges inobtaining an accurate picture of the traffic conditions in real time. During the last decade,the technical development of mobile platforms and the growing online connectivity made itpossible to obtain traffic information from social media and applications based on spatialcrowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receiveinformation about a more comprehensive number of incidents and can monitor areaswhich are not covered by the conventional incident detection systems. The crowdsourcedtraffic data can provide supplementary information for incidents already reported throughother sources and it can contribute to earlier detection of incidents, which can lead tofaster response and clearance time. Furthermore, spatial crowdsourcing can help to detectincident types, which are not collected systematically yet (e.g. potholes, traffic light faults,missing road signs). However, before exploiting crowdsourced traffic data in trafficmanagement, numerous challenges need to be resolved, such as verification of the incidentreports, predicting the severity of the crowdsourced incidents and integration with trafficdata obtained from other sources.During this thesis, the possibilities and challenges of utilizing spatial crowdsourcingtechnologies to detect non-recurring incidents were examined in form of a case study.Traffic incident alerts obtained from Waze, a navigation application using the concept ofcrowdsourcing, were analyzed and compared with officially verified incident reports inStockholm. The thesis provides insight into the spatial and temporal characteristics of theWaze data. Moreover, a method to identify related Waze alerts and to determine matchingincident reports from different sources is presented. The results showed that the number ofreported incidents in Waze is 4,5 times higher than the number of registered incidents bythe Swedish authorities. Furthermore, 27,5 % of the incidents could have been detectedfaster by using the traffic alerts from Waze. In addition, the severity of Waze alerts isexamined depending on the attributes of the alerts.

The early observation and elimination of non-recurring incidents is a crucial task in traffic management. The performance of the conventional incident detection methods (traffic cameras and other sensory technologies) is limited and there are still challenges in obtaining an accurate picture of the traffic conditions in real time. During the last decade, the technical development of mobile platforms and the growing online connectivity made it possible to obtain traffic information from social media and applications based on spatial crowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receive information about a more comprehensive number of incidents and can monitor areas which are not covered by the conventional incident detection systems. The crowdsourced traffic data can provide supplementary information for incidents already reported through other sources and it can contribute to earlier detection of incidents, which can lead to faster response and clearance time. Furthermore, spatial crowdsourcing can help to detect incident types, which are not collected systematically yet (e.g. potholes, traffic light faults, missing road signs). However, before exploiting crowdsourced traffic data in traffic management, numerous challenges need to be resolved, such as verification of the incident reports, predicting the severity of the crowdsourced incidents and integration with traffic data obtained from other sources. During this thesis, the possibilities and challenges of utilizing spatial crowdsourcing technologies to detect non-recurring incidents were examined in form of a case study. Traffic incident alerts obtained from Waze, a navigation application using the concept of crowdsourcing, were analyzed and compared with officially verified incident reports in Stockholm. The thesis provides insight into the spatial and temporal characteristics of the Waze data. Moreover, a method to identify related Waze alerts and to determine matching incident reports from different sources is presented. The results showed that the number of reported incidents in Waze is 4,5 times higher than the number of registered incidents by the Swedish authorities. Furthermore, 27,5 % of the incidents could have been detected faster by using the traffic alerts from Waze. In addition, the severity of Waze alerts is examined depending on the attributes of the alerts.

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.

<p dir="ltr">Traffic Incident Management (TIM) is an important tool for agencies to reduce secondary crashes, improve travel reliability, and ensure safety of first responders. Having “eyes” on the scene from roadside traffic cameras can assist operators to dispatch appropriate personnel, provide situational awareness, and allow for quick response when incident conditions change. Many intelligent traffic systems (ITS) centers deploy pan-tilt-zoom (PTZ) cameras that provide broad coverage but require operators to position. When incidents occur or a public safety vehicle stops for roadside assistance, Traffic Management Center (TMC) operators need to reposition cameras to monitor the event. The camera positioning time depends on operator experience, accuracy of 911 call, location, public safety radio reports, and in some cases, GPS positions. This research outlines the methodology to use GPS data sources to automate camera position to a scene for event nature verification. In general, this GPS information can come from either connected vehicles or public safety vehicles, such as Indiana Department of Transportation (INDOT) Hoosier Helpers. Implementing this research into INDOT daily operations has increased the number of events that cameras verify, while decreasing the time from event occurrence to camera verification from a median of 5 minutes to a median of approximately 90 seconds. The time is driven by the accuracy and frequency of GPS data from devices. With increased telematics polling rates and availability of enhanced vehicle data such as door open/close and seatbelt latch events, this latency is expected to further decline. </p>

In current study, a mechanism to extract traffic related information such as congestion and incidents from textual data from the internet is proposed. The current source of data is Twitter, however, the same mechanism can be extended to any kind of text available on the internet. As the data being considered is extremely large in size automated models are developed to stream, download, and mine the data in real-time. Furthermore, if any tweet has traffic related information then the models should be able to infer and extract this data. To pursue this task, Artificial Intelligence, Machine Learning, and Natural Language Processing techniques are used. These models are designed in such a way that they are able to detect the traffic congestion and traffic incidents from the Twitter stream at any location. Currently, the data is collected only for United States. The data is collected for 85 days (50 complete and 35 partial) randomly sampled over the span of five months (September, 2014 to February, 2015) and a total of 120,000 geo-tagged traffic related tweets are extracted, while six million geo-tagged non-traffic related tweets are retrieved. The classification models for detection of traffic congestion and incidents are trained on this dataset. Furthermore, this data is also used for various kinds of spatial and temporal analysis. A mechanism to calculate level of traffic congestion, safety, and traffic perception for cities in U.S. is proposed. Traffic congestion and safety rankings for the various urban areas are obtained and then they are statistically validated with existing widely adopted rankings. Traffic perception depicts the attitude and perception of people towards the traffic. It is also seen that traffic related data when visualized spatially and temporally provides the same pattern as the actual traffic flows for various urban areas. When visualized at the city level, it is clearly visible that the flow of tweets is similar to flow of vehicles and that the traffic related tweets are representative of traffic within the cities. With all the findings in current study, it is shown that significant amount of traffic related information can be extracted from Twitter and other sources on internet. Furthermore, Twitter and these data sources are freely available and are not bound by spatial and temporal limitations. That is, wherever there is a user there is a potential for data.

In 2018 the Utah Department of Transportation (UDOT) funded a study in which data were collected to evaluate performance measures for UDOT's Incident Management Team (IMT) program. After that study was completed, UDOT received funding to expand the size of its IMT program. Additionally, TransSuite, a data source used by the UDOT Traffic Operations Center to log incident-related data, was reconfigured to provide a higher quantity of performance measure data. This study made use of the new data source, in addition to Computer Aided Dispatch logs provided by the Utah Highway Patrol that were used in the first study, to collect performance measure data of the expanded program and measure the impacts of the IMT program expansion. Using these two datasets, a reanalyzed 2018 dataset and a new 2020 dataset, a comparison of performance measures was made. Performance measures studied included those defined as important by the Federal Highway Administration's Focus States Initiative in 2009, namely Roadway Clearance Time, Incident Clearance Time, and Response Time. These performance measures were calculated for IMT responders at 320 incidents in 2018 and 289 incidents in 2020. In addition, data regarding the affected volume associated with incidents, the excess travel time accumulated due to incidents, and the excess user cost associated with incident congestion were gathered. In 2018, 188 incidents were analyzed for these user impacts, and in 2020 144 incidents were analyzed. Statistical analyses were conducted to compare IMT performance between the two years and to determine relationships between performance measures and user impacts. The effects of the COVID-19 pandemic affected traffic volumes during this study, and statistical analyses were adjusted to account for volume differences between the two years. Results indicated that the expansion of the IMT program has allowed UDOT to respond faster to incidents, and respond to a larger quantity of incidents over a larger coverage area and in extended operating hours. Performance of the expanded IMT program has had significant effects in reducing incident-related congestion and its costs.

In 2009 the Federal Highway Administration published a report regarding a Focus States Initiative that had been conducted with 11 states to discuss the development of national Traffic Incident Management (TIM) standards. Performance measures were defined, and a national TIM dashboard created, but very little data has been added to the dashboard since. In this research study, performance measures of the Utah Department of Transportation (UDOT) TIM program were analyzed. Data availability was first assessed to determine whether these performance measures could be calculated. It was determined that crash response data available from the Utah Highway Patrol (UHP) could be used to calculate the performance measures of Incident Management Teams (IMT) and UHP units; however, roadway clearance data were missing. UHP personnel agreed to collect additional data regarding crash roadway clearance for six months of the study. Performance measures of response time (RT), roadway clearance time (RCT), and incident clearance time (ICT) were calculated for responding units at 168 crashes. Using the crash response data from UHP and traffic speed, travel time, and volume data from UDOT databases, 83 of the 163 crashes that met additional criteria were evaluated to determine the volume of traffic affected (AV) by each incident and the associated user cost (EUC). Statistical analyses to determine relationships between different measures such as RT, RCT, ICT, AV, and EUC were conducted to assist UDOT in optimizing the allocation of their IMT resources.

In 2019, the Utah Department of Transportation (UDOT) funded a research study evaluating the performance measures of UDOT's expanded Incident Management Team (IMT) program. The number of IMTs patrolling Utah roadways increased from 13 to 25 between 2018 and 2020. Crash data were collected from the Utah Highway Patrol's Computer Aided Dispatch database and from the UDOT TransSuite database to compare IMT performance measures between the two years and to evaluate the benefits of the expanded IMT program. However, these data were compromised due to the effects of the COVID-19 pandemic. This study collected data for 2022 using the same methodology as the Phase II study to compare IMT performance measures in 2022 with those of 2018 after traffic volumes had returned to a similar level as those of pre-pandemic levels. There were 283 and 307 incidents for the years of 2018 and 2022, respectively, that were analyzed for IMT performance measures which include response time, roadway clearance time, and incident clearance time. There were 172 and 236 incidents for the years of 2018 and 2022, respectively, that were analyzed for user impacts which were affected volume, excess travel time, and excess user costs. Results of the statistical analyses conducted on the 2018 and 2022 datasets show that IMTs can respond more quickly to incidents in a larger coverage area with significantly reduced user impacts. The expanded IMT program is also able to respond to more incidents, including those of high severity, while significantly decreasing congestion.

Any situation that causes traffic to stop or slow down creates cost for society, both in terms of lost time for the road users and increased CO2 emissions. Trafik Stockholm, a traffic management centre for Stockholm collectively run by STA, Stockholm city and Nacka municipality therefore have the responsibility to facilitate the removal of such situations. One of the means by which they do that is by sending out road assistance vehicles (RAVs) to help clearing these situations. In order to do so effectively there is a need to know the effect of emergency lights on the time it takes for the RAV to reach its destination. This thesis thus examines how the emergency lights affect the speed at which the RAVs can travel by comparing the actual speed at which the RAVs have travelled using emergency lights to the mean speed of traffic on the same highway at the same time. It is concluded that there might be a significant effect, but further studies are necessary to prove this statistically.

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.