Locating Intelligent Sensors on a Transportation Network to Facilitate Emergency Response to Traffic Incidents

2015 ◽  
pp. 191-222
Author(s):  
Tejswaroop Geetla ◽  
Rajan Batta ◽  
Alan Blatt ◽  
Marie Flanigan ◽  
Kevin Majka
Keyword(s):  
Emergency Response ◽  
Transportation Network ◽  
Traffic Incidents ◽  
Intelligent Sensors

scholarly journals A Descriptive Analysis on the Impact of COVID-19 Lockdowns on Road Traffic Incidents in Sydney, Australia

2021 ◽  
Vol 18 (21) ◽  
pp. 11701
Author(s):  
Sai Chand ◽  
Ernest Yee ◽  
Abdulmajeed Alsultan ◽  
Vinayak V. Dixit
Keyword(s):  
Large Scale ◽  
Road Traffic ◽  
Transportation Network ◽  
Sustainable Transportation ◽  
Traffic Incidents ◽  
Traffic Incident ◽  
Incident Duration ◽  
The Mean ◽  
The Impact ◽  
Sydney Australia

COVID-19 has had tremendous effects worldwide, resulting in large-scale death and upheaval. An abundance of studies have shown that traffic patterns have changed worldwide as working from home has become dominant, with many facilities, restaurants and retail services being closed due to the lockdown orders. With regards to road safety, there have been several studies on the reduction in fatalities and crash frequencies and increase in crash severity during the lockdown period. However, no scientific evidence has been reported on the impact of COVID-19 lockdowns on traffic incident duration, a key metric for crash management. It is also unclear from the existing literature whether the impacts on traffic incidents are consistent across multiple lockdowns. This paper analyses the impact of two different COVID-19 lockdowns in Sydney, Australia, on traffic incident duration and frequency. During the first (31 March–28 April 2020) and second (26 June–31 August 2021) lockdowns, the number of incidents fell by 50% and 60%, respectively, in comparison to the same periods in 2018 and 2019. The proportion of incidents involving towing increased significantly during both lockdowns. The mean duration of crashes increased by 16% during the first lockdown, but the change was less significant during the subsequent lockdown. Crashes involving diversions, emergency services and towing saw an increase in the mean duration by 67%, 16%, and 47%, respectively, during the first lockdown. However, this was not reflected in the 2021 data, with only major crashes seeing a significant increase, i.e., by 58%. There was also a noticeable shift in the location of incidents, with more incidents recorded in suburban areas, away from the central business area. Our findings suggest drastic changes in incident characteristics, and these changes should be considered by policymakers in promoting a safer and more sustainable transportation network in the future.

Algorithm to prioritize the restoration of a multiple facility, multiple-hazard road network

2017 ◽  
Vol 231 (3) ◽  
pp. 221-231 ◽  
Author(s):  
Veeresh Varad Basavaraj ◽  
Venkateswaran Shekar ◽  
Lance Fiondella ◽  
Ashrafur Rahman ◽  
Nicholas Lownes
Keyword(s):  
Emergency Response ◽  
Road Network ◽  
Emergency Services ◽  
Response Times ◽  
Transportation Network ◽  
Assessment Process ◽  
Critical Infrastructures ◽  
Risk Assessment Process ◽  
Network Restoration ◽  
Different Types

Transportation networks are one of several critical infrastructures on which first responders rely in order to deliver emergency services. However, there is no guarantee that a transportation network will be fully operational following a regional event, such as a hurricane or earthquake. Emergency planning and response tools should explicitly integrate this possibility to ensure the completeness of the risk assessment process. This paper considers the elevated vulnerability to which a community is exposed when disruptions in a transportation network slow emergency response. An average weighted vulnerability metric is defined to favor a network restoration strategy that quickly reduces emergency response times to their nominal levels. This metric is incorporated into an algorithm to prioritize network restoration, so that individuals who might require assistance are not exposed to prolonged periods of lengthy response times. The formulation considers a number of types of emergency response facilities and different types of hazard; the hazards can require a response from more than one type of facility. The utility of the approach is illustrated through a series of examples. Our results demonstrate that the approach can offer systematic guidance to reduce emergency response times after a partial or complete outage of a transportation network.

Optimal placement of omnidirectional sensors in a transportation network for effective emergency response and crash characterization

2014 ◽  
Vol 45 ◽  
pp. 64-82 ◽  
Author(s):  
Tejswaroop Geetla ◽  
Rajan Batta ◽  
Alan Blatt ◽  
Marie Flanigan ◽  
Kevin Majka
Keyword(s):  
Emergency Response ◽  
Transportation Network ◽  
Optimal Placement

scholarly journals Hazmats Transportation Network Design Model with Emergency Response under Complex Fuzzy Environment

2013 ◽  
Vol 2013 ◽  
pp. 1-22 ◽  
Author(s):  
Jiuping Xu ◽  
Jun Gang ◽  
Xiao Lei
Keyword(s):  
Network Design ◽  
Emergency Response ◽  
Transportation Network ◽  
Fuzzy Variable ◽  
Optimal Solution ◽  
Transportation Cost ◽  
Bilevel Optimization ◽  
Practical Case ◽  
Transportation Risk ◽  
Bee Colony

A bilevel optimization model for a hazardous materials transportation network design is presented which considers an emergency response teams location problem. On the upper level, the authority designs the transportation network to minimize total transportation risk. On the lower level, the carriers first choose their routes so that the total transportation cost is minimized. Then, the emergency response department locates their emergency service units so as to maximize the total weighted arc length covered. In contrast to prior studies, the uncertainty associated with transportation risk has been explicitly considered in the objective function of our mathematical model. Specifically, our research uses a complex fuzzy variable to model transportation risk. An improved artificial bee colony algorithm with priority-based encoding is also applied to search for the optimal solution to the bilevel model. Finally, the efficiency of the proposed model and algorithm is evaluated using a practical case and various computing attributes.

Transportation network reliability in emergency response

2015 ◽  
Vol 80 ◽  
pp. 56-73 ◽  
Author(s):  
Ali Edrissi ◽  
Mehdi Nourinejad ◽  
Matthew J. Roorda
Keyword(s):  
Emergency Response ◽  
Network Reliability ◽  
Transportation Network

Near Real-Time Flood Impact Analysis on Road Networks

2021 ◽  
Author(s):  
Martijn Kwant ◽  
Frederique de Groen ◽  
Margreet van Marle ◽  
Arjen Haag ◽  
Herman Haaksma
Keyword(s):  
Real Time ◽  
Emergency Response ◽  
Access To Health Care ◽  
Large Scale ◽  
Impact Analysis ◽  
Transportation Network ◽  
Early Warning Systems ◽  
Flood Hazards ◽  
Cascading Effects ◽  
The Impact

<p>Traditional flood risk studies often focus on direct economic impact, such as property damage or agricultural loss. However, the impact of floods is not limited to these direct damages. In fact societal costs and/or cascading effects are often much higher than the direct impact of floods. Cascading effects, such as access to healthcare and infrastructure accessibility are vital components for efficient emergency response management. This requires methodologies to quickly analyze the impact of large-scale floods on infrastructure networks.</p><p> </p><p>In this case study, the use of satellite-based flood maps are examined in combination with network criticality in the Mandalay region in central Myanmar. This region was severely affected by flooding after heavy monsoon rains in 2019. Many regions in the world are affected by this type of floods every year, resulting in large scale evacuations and limited access to health care. During these type of events, the transportation network is a crucial part for emergency response, as it is used for the delivery of goods, evacuation and deployment of emergency hospitals.</p><p> </p><p>The core of this study is a methodology to assess near real-time flood extents based on Sentinel-1 satellite imagery and the impact on network criticality. These tools were used to analyze the redundancy of the infrastructure network and quantify cascading impacts of flood hazards such as road accessibility and access to medical services. The methodology shows potential for operational use by linking with flood early warning systems (e.g. Delft-FEWS) enabling impact-based forecasting.</p>

Sign in / Sign up

Export Citation Format

Share Document