Risk Minimizing Evacuation Strategies under Uncertainty

This paper presents results on the simulation of the evacuation of the city of Padang with approximately 1,000,000 inhabitants. The model used is MATSim (www.matsim.org). Three different strategies were applied: shortest path solution, user optimum, system optimum, together with a constraint that moves should reduce risk whenever possible. The introduction of the risk minimization increases the overall required safe egress time (RSET). The differences between the RSET for the three risk minimizing strategies are small. Further quantities used for the assessment of the evacuation are the formation of congestion and the individual RSETs (in comparison with the available SET).BMBF, 03G0666E, Verbundprojekt FW: Last-mile Evacuation; Vorhaben: Evakuierungsanalyse und Verkehrsoptimierung, Evakuierungsplan einer Stadt - Sonderprogramm GEOTECHNOLOGIENBMBF, 03NAPAI4, Transport und Verkehr: Verbundprojekt ADVEST: Adaptive Verkehrssteuerung; Teilprojekt Verkehrsplanung und Verkehrssteuerung in Megacitie

The MATSim Network Flow Model for Traffic Simulation Adapted to Large-Scale Emergency Egress and an Application to the Evacuation of the Indonesian City of Padang in Case of a Tsunami Warning

The evacuation of whole cities or even regions is an important problem, as demonstrated by recent events such as evacuation of Houston in the case of Hurricane Rita or the evacuation of coastal cities in the case of Tsunamis. This paper describes a complex evacuation simulation framework for the city of Pandang, with approximately 1,000,000 inhabitants. Padang faces a high risk of being inundated by a tsunami wave. The evacuation simulation is based on the MATSim framework for large-scale transport simulations. Different optimization parameters like evacuation distance, evacuation time, or the variation of the advance warning time are investigated. The results are given as overall evacuation times, evacuation curves, an detailed GIS analysis of the evacuation directions. All these results are discussed with regard to their usability for evacuation recommendations.BMBF, 03G0666E, Verbundprojekt FW: Last-mile Evacuation; Vorhaben: Evakuierungsanalyse und Verkehrsoptimierung, Evakuierungsplan einer Stadt - Sonderprogramm GEOTECHNOLOGIENBMBF, 03NAPAI4, Transport und Verkehr: Verbundprojekt ADVEST: Adaptive Verkehrssteuerung; Teilprojekt Verkehrsplanung und Verkehrssteuerung in Megacitie

Safety Aware Vehicle Routing Algorithm, A Weighted Sum Approach

Driving is an essential part of work life for many people. Although driving can be enjoyable and pleasant, it can also be stressful and dangerous. Many people around the world are killed or seriously injured while driving. According to the World Health Organization (WHO), about 1.25 million people die each year as a result of road traffic crashes. Road traffic injuries are also the leading cause of death among young people. To prevent traffic injuries, governments must address road safety issues, an endeavor that requires involvement from multiple sectors (transport, police, health, education). Effective intervention should include designing safer infrastructure and incorporating road safety features into land-use and transport planning. The aim of this research is to design an algorithm to help drivers find the safest path between two locations. Such an algorithm can be used to find the safest path for a school bus travelling between bus stops, a heavy truck carrying inflammable materials, poison gas, or explosive cargo, or any driver who wants to avoid roads with higher numbers of accidents. In these applications, a path is safe if the danger factor on either side of the path is no more than a given upper bound. Since travel time is another important consideration for all drivers, the suggested algorithm utilizes traffic data to consider travel time when searching for the safest route. The key achievements of the work presented in this thesis are summarized as follows. Defining the Safest and Quickest Path Problem (SQPP), in which the goal is to find a short and low-risk path between two locations in a road network at a given point of time. Current methods for representing road networks, travel times and safety level were investigated. Two approaches to defining road safety level were identified, and some methods in each approach were presented. An intensive review of traffic routing algorithms was conducted to identify the most well-known algorithms. An empirical study was also conducted to evaluate the performance of some routing algorithms, using metrics such as scalability and computation time. This research approaches the SQPP problem as a bi-objective Shortest Path Problem (SPP), for which the proposed Safety Aware Algorithm (SAA) aims to output one quickest and safest route. The experiments using this algorithm demonstrate its efficacy and practical applicability