Strategic Evacuation for Hurricanes and Regional Events with and without Autonomous Vehicles

2021 ◽  
pp. 036119812110074
Author(s):  
Jooyong Lee ◽  
Kara M. Kockelman
Keyword(s):  
Travel Time ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Autonomous Vehicle ◽  
Time Span ◽  
Network Congestion ◽  
Travel Times ◽  
Total Delay ◽  
Evacuation Time ◽  
Conflicting Objectives

A scheduling algorithm is developed for optimal planning of large-scale, complex evacuations to minimize total delay plus travel time across residents. The algorithm is applied to the eight-county Houston-Galveston region and land use setting under the 2017 Hurricane Harvey scenario with multiple destinations. Autonomous vehicle (AV) use under central guidance is also tested, to demonstrate the evacuation time benefits of AVs. Higher share of AVs delivers more efficient evacuation performance, thanks to greater reliability on evacuation order compliance, lower headways, and higher road capacity. Furthermore, 100% AV use delivers lower overall evacuation costs and network clearance times and less uncertainty in travel times (via lower standard deviation in). Based on evaluations of different evacuation schedules, a 50% compressed evacuation time span resulted in longer travel times and network congestion. A 50% longer evacuation time span reduced residents' total travel time and network congestion, but increased the evacuation cost. As expected, evacuation efficiency falls when evacuees do not comply with evaucation schedules. Large shares of AVs will not be possible in the near future, so methods to enhance evacuees' compliance behavior (e.g., enforced and prioritized evacuation orders) should be considered until a meaningful level of AV technical maturity and penetration rate is available. This paper demonstrates the benefits of scheduled departure times, AV use, and evacuation order compliance, which help balance conflicting objectives during emergencies.

Appraising relative earthquake location errors

1992 ◽  
Vol 82 (2) ◽  
pp. 836-859 ◽  
Author(s):  
Gary L. Pavlis
Keyword(s):  
Travel Time ◽  
Upper Bound ◽  
Large Scale ◽  
Velocity Structure ◽  
Order Approximation ◽  
Earthquake Location ◽  
Travel Times ◽  
Spatial Error ◽  
Location Errors ◽  
Error Terms

Abstract Earthquake location estimates suffer from two types of errors: (1) systematic offsets caused by large scale earth structure, and (2) scatter of locations of different earthquakes relative to each other. I show that relative location errors are controlled by four separate error terms: (1) scatter caused by random, measurement error; (2) nonlinear effects; (3) mislocations caused by interaction of errors in modeling travel times with variations in the number and quality of arrivals recorded by different events; and (4) mislocations caused by variations in how errors in modeling travel times vary with position inside the real Earth. The first can be handled by conventional statistical methods. The second can be bounded using a second-order approximation, provided one can provide a reasonable estimate for an upper bound on the total spatial error that might be present in the location estimate. I demonstrate that the size of each of the two error terms related to inadequate knowledge of the Earth's velocity structure can be bounded provided we can determine an upper bound on travel-time errors as a function of distance. I describe an empirical approach for determining such a bound using differences between the sum of squared residuals of earthquakes located with all available data and the same event located with a single arrival deleted. This calculation is repeated for all arrivals and used to construct an upper bound on travel-time errors as a function of distance. The concepts developed are applied to bound errors in locations of earthquakes in the Garm region of central Asia, and they demonstrate the utility of these ideas in sorting out events with minimal relative error.

Evaluation Results of the Amsterdam, Netherlands, Practical Trial with In-Car Travel and Route Advice

2017 ◽  
Vol 2621 (1) ◽  
pp. 38-45 ◽  
Author(s):  
Isabel Wilmink ◽  
Eline Jonkers ◽  
Maaike Snelder ◽  
Gerdien Klunder
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Large Scale ◽  
User Behavior ◽  
Route Guidance ◽  
Travel Times ◽  
Guidance Services ◽  
Social Navigation ◽  
Total Travel Time ◽  
Message Signs

Travel and route guidance services are widely available. Social navigation services that provide travelers with advice aimed at minimizing driver travel time, while also taking into account the effect on travel times of other travelers, are relatively new. Theoretically, social navigation has been shown to reduce total travel time by 10% to 30%. This paper presents the evaluation results of a large-scale field trial for pretrip and on-trip route advice with load balancing, in which about 20,000 participants were active. The evaluation provided insight into the potential effects of in-car information services, such as effects on user behavior, traffic flow effects, and technical aspects. Participants used mostly the pretrip advisories. Compliance with the on-trip route advice was 50%, which was considered high (compared with compliance with route advice on variable message signs). An effect on traffic flow could not be measured, as penetration rates were (despite thousands of users) still too low. An offline study using measured travel times combined with a traffic model, however, showed that substantial delay reductions can be achieved for the Amsterdam, Netherlands, region. Participants’ appreciation of the service resulted in a mixed picture with positive and negative ratings. The main practical contribution of this paper is that the results can be used to develop social navigation services. Empirical insights about route advice compliance can be seen as the main scientific contribution.

scholarly journals Minimization of Delay and Travel Time of Yard Trucks in Container Terminals Using an Improved GA with Guidance Search

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Z. X. Wang ◽  
Felix T. S. Chan ◽  
S. H. Chung ◽  
Ben Niu
Keyword(s):  
Travel Time ◽  
Large Scale ◽  
Container Terminal ◽  
Container Terminals ◽  
Computational Time ◽  
Small Scale ◽  
Storage Allocation ◽  
Total Delay ◽  
Truck Scheduling ◽  
Np Hard Problem

Yard truck scheduling and storage allocation problems (YTS-SAP) are two important issues that influence the efficiency of a container terminal. These two problems aim to determine the routing of trucks and proper storage locations for discharging containers from incoming vessels. This paper integrates YTS and SAP as a whole and tries to minimize the weighted summation of total delay and total yard trucks travel time. A genetic algorithm (GA) is proposed to deal with the problem. In the proposed GA, guidance mutation approach and exhaustive heuristic for local searching are used in order to force the GA to converge faster and be steadier. To test the performance of the proposed GA, both small scale and large scale cases are studied. The results of these cases are compared with CPLEX for the small scale cases. Since this problem is an NP-hard problem, which CPLEX cannot solve, a simple GA is studied for comparison in large scale cases. The comparison demonstrates that the proposed GA can obtain near optimal solutions in much shorter computational time for small scale cases. In addition, the proposed GA can obtain better results than other methods in reasonable time for large scale cases.

Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Yan Pan ◽  
Shining Li ◽  
Qianwu Chen ◽  
Nan Zhang ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Load Capacity ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Promising Alternative ◽  
Logistics Industry ◽  
Last Mile ◽  
Delivery Scheduling ◽  
Schedule Approach

Stimulated by the dramatical service demand in the logistics industry, logistics trucks employed in last-mile parcel delivery bring critical public concerns, such as heavy cost burden, traffic congestion and air pollution. Unmanned Aerial Vehicles (UAVs) are a promising alternative tool in last-mile delivery, which is however limited by insufficient flight range and load capacity. This paper presents an innovative energy-limited logistics UAV schedule approach using crowdsourced buses. Specifically, when one UAV delivers a parcel, it first lands on a crowdsourced social bus to parcel destination, gets recharged by the wireless recharger deployed on the bus, and then flies from the bus to the parcel destination. This novel approach not only increases the delivery range and load capacity of battery-limited UAVs, but is also much more cost-effective and environment-friendly than traditional methods. New challenges therefore emerge as the buses with spatiotemporal mobility become the bottleneck during delivery. By landing on buses, an Energy-Neutral Flight Principle and a delivery scheduling algorithm are proposed for the UAVs. Using the Energy-Neutral Flight Principle, each UAV can plan a flying path without depleting energy given buses with uncertain velocities. Besides, the delivery scheduling algorithm optimizes the delivery time and number of delivered parcels given warehouse location, logistics UAVs, parcel locations and buses. Comprehensive evaluations using a large-scale bus dataset demonstrate the superiority of the innovative logistics UAV schedule approach.

scholarly journals “In cities, it’s not far, but it takes long”: comparing estimated and replicated travel times to reach life-saving obstetric care in Lagos, Nigeria

2021 ◽  
Vol 6 (1) ◽  
pp. e004318
Author(s):  
Aduragbemi Banke-Thomas ◽  
Kerry L M Wong ◽  
Francis Ifeanyi Ayomoh ◽  
Rokibat Olabisi Giwa-Ayedun ◽  
Lenka Benova
Keyword(s):  
Travel Time ◽  
Friction Surface ◽  
Obstetric Care ◽  
Travel Times ◽  
Night Time ◽  
Google Maps ◽  
Surface Approach ◽  
Low Resource ◽  
Life Saving ◽  
The Cost

BackgroundTravel time to comprehensive emergency obstetric care (CEmOC) facilities in low-resource settings is commonly estimated using modelling approaches. Our objective was to derive and compare estimates of travel time to reach CEmOC in an African megacity using models and web-based platforms against actual replication of travel.MethodsWe extracted data from patient files of all 732 pregnant women who presented in emergency in the four publicly owned tertiary CEmOC facilities in Lagos, Nigeria, between August 2018 and August 2019. For a systematically selected subsample of 385, we estimated travel time from their homes to the facility using the cost-friction surface approach, Open Source Routing Machine (OSRM) and Google Maps, and compared them to travel time by two independent drivers replicating women’s journeys. We estimated the percentage of women who reached the facilities within 60 and 120 min.ResultsThe median travel time for 385 women from the cost-friction surface approach, OSRM and Google Maps was 5, 11 and 40 min, respectively. The median actual drive time was 50–52 min. The mean errors were >45 min for the cost-friction surface approach and OSRM, and 14 min for Google Maps. The smallest differences between replicated and estimated travel times were seen for night-time journeys at weekends; largest errors were found for night-time journeys at weekdays and journeys above 120 min. Modelled estimates indicated that all participants were within 60 min of the destination CEmOC facility, yet journey replication showed that only 57% were, and 92% were within 120 min.ConclusionsExisting modelling methods underestimate actual travel time in low-resource megacities. Significant gaps in geographical access to life-saving health services like CEmOC must be urgently addressed, including in urban areas. Leveraging tools that generate ‘closer-to-reality’ estimates will be vital for service planning if universal health coverage targets are to be realised by 2030.

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

2021 ◽  
pp. 036119812110184
Author(s):  
Monika Filipovska ◽  
Hani S. Mahmassani ◽  
Archak Mittal
Keyword(s):  
Time Dependence ◽  
Travel Time ◽  
Correlation Structure ◽  
Time Varying ◽  
Travel Times ◽  
Trajectory Data ◽  
Time Varying Networks ◽  
Transportation Research ◽  
Search Approach ◽  
Stochastic Time

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

Simulation on Rationalization Process Scheduling in Large-Scale Civil Engineering

2014 ◽  
Vol 602-605 ◽  
pp. 571-574
Author(s):  
Mao Liu
Keyword(s):  
Large Scale ◽  
Civil Engineering ◽  
Scheduling Algorithm ◽  
Construction Process ◽  
Process Scheduling

In the construction process of large-scale civil engineering and architecture, how to realize rational scheduling is a key problem need to be solved. This paper studies the construction process of the large-scale Civil Engineering decoration companies, particularly the construction with parallel multiple sets of team and multi-project. To solve the problem, the paper designs a special scheduling algorithm and carries out simulation. The scheduling algorithm shorts the duration of construction and improves enterprise efficiency.

scholarly journals Equity Effects of Congestion Charges: An Exploratory Analysis with MATSim

2017 ◽  
Vol 2670 (1) ◽  
pp. 75-82 ◽  
Author(s):  
Lucas Meyer de Freitas ◽  
Oliver Schuemperlin ◽  
Milos Balac ◽  
Francesco Ciari
Keyword(s):  
Travel Time ◽  
Large Scale ◽  
Agent Based ◽  
Promising Tool ◽  
Travel Time Savings ◽  
Time Savings ◽  
Time Changes ◽  
High Level ◽  
Congestion Charging ◽  
Equity Effects

This paper shows an application of the multiagent, activity-based transport simulation MATSim to evaluate equity effects of a congestion charging scheme. A cordon pricing scheme was set up for a scenario of the city of Zurich, Switzerland, to conduct such an analysis. Equity is one of the most important barriers toward the implementation of a congestion charging system. After the challenges posed by equity evaluations are examined, it is shown that agent-based simulations with heterogeneous values of time allow for an increased level of detail in such evaluations. Such detail is achieved through a high level of disaggregation and with a 24-h simulation period. An important difference from traditional large-scale models is the low degree of correlation between travel time savings and welfare change. While traditional equity analysis is based on travel time savings, MATSim shows that choice dimensions not included in traditional models, such as departure time changes, can also play an important role in equity effects. The analysis of the results in light of evidence from the literature shows that agent-based models are a promising tool to conduct more complete equity evaluations not only of congestion charges but also of transport policies in general.

scholarly journals Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cong Bai ◽  
Zhong-Ren Peng ◽  
Qing-Chang Lu ◽  
Jian Sun
Keyword(s):  
Travel Time ◽  
Dynamic Model ◽  
Kalman Filtering ◽  
Prediction Models ◽  
Support Vector ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Real World Data ◽  
Time Prediction ◽  
Bus Travel Time

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

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