Analyzing travel time reliability and its influential factors of emergency vehicles with generalized extreme value theory

Zhang, Zhenhua; He, Qing; Gou, Jizhan; Li, Xiaoling

doi:10.1080/15472450.2018.1473156

Cited by 13 publications

(5 citation statements)

References 30 publications

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“…Similar to SVs, EMVs usually select the best route between the emergency station and rescue spot to maximize their utilities. However, in addition to the differences in vehicle size, vehicle dynamics, and driving policies mentioned before, EMVs also differ from SVs as EMVs are more concerned with travel times [14], safety risks [51], and reliabilities [52], rather than comfort, fuel consumption, and travel distance in route optimization objectives. To better understand this process, we divided it into three stages: environmental perception, feature extraction, and algorithm selection, as illustrated in Figure 6, accompanied by realworld scenarios.…”

Section: Emv-ro Algorithmsmentioning

confidence: 99%

“…Relying on overly strong model assumptions [28] and ignorance of the different characteristics of EMVs and SVs will significantly reduce the accuracy of models and algorithms, causing a disconnect between the optimized results and the real-world requirements. In the context of the big data era, future research should fully leverage the advantages of data and concentrate on uncovering the authentic rescue demand characteristics [26] and routing preferences [14,51,52] by collecting and mining actual EMV data (e.g., trajectory data, alarm data from the emergency department) to close the divide between the proposed algorithm and its real-world implementation [27,36].…”

Section: Uncovering Authentic Demand Characteristics Through Emv Data...mentioning

confidence: 99%

“…Apart from the single indicator of response times, future research in terms of EMV-RO methods can incorporate additional objectives [14,51,52], including minimizing driving risk, enhancing reliability, and mitigating negative impacts on SVs [71,72,90]. Concurrently, future research could select appropriate solving algorithms and incorporate EMVs' distinct driving policies and vehicle attributes into the routing optimization model [99], which could augment the efficiency and accuracy of achieving optimal routes [98].…”

Section: Incorporating Distinct Characteristics Of Emvs In Emv-ro Modelsmentioning

confidence: 99%

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Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

Hao,

Wang,

Yang

2024

Sustainability

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Emergency vehicles (EMVs) play an important role in saving human lives and mitigating property losses in urban traffic systems. Due to traffic congestion and improper priority control strategies along the rescue route, EMVs may not be able to arrive at rescue spots on time, which also increases traffic risk and has a negative impact on social vehicles (SVs). The greater the negative impact on SVs, such as increased delay times and queue length, the more profound the negative impacts on urban environmental sustainability. Proper rescue route selection and priority control strategies are essential for addressing this problem. Consequently, this paper systematically reviews the studies on EMV routing and priority control. First, a general bibliometric analysis is conducted using VOSviewer. This study also classifies the existing studies into three parts: EMV travel time prediction (EMV-TTP), EMV routing optimization (EMV-RO), and EMV traffic priority control (EMV-TPC). Finally, this study provides future research suggestions on five aspects: 1. uncovering authentic demand characteristics through EMV data mining, 2. incorporating the distinct characteristics of EMV in EMV-RO models, 3. implementing active EMV-TPC strategies, 4. concentrating more on the negative impacts on SVs, and 5. embracing the emerging technologies in the future urban traffic environment.

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Section: Emv-ro Algorithmsmentioning

confidence: 99%

Section: Uncovering Authentic Demand Characteristics Through Emv Data...mentioning

confidence: 99%

Section: Incorporating Distinct Characteristics Of Emvs In Emv-ro Modelsmentioning

confidence: 99%

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Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

Hao,

Wang,

Yang

2024

Sustainability

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“…There are several drawbacks of earlier models, such as the necessity to rely on simplifying assumptions for fundamental issues, e.g., coverage of emergency cases, relocation of ambulances, and busy probabilities [1]. Many models ignore patient survivability [2], uncertainties linked with travel times, or route choice [3].…”

Section: Introductionmentioning

confidence: 99%

On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia

Buzna

Czimmermann

2021

Mathematics

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The efficient operation of emergency medical services is critical for any society. Typically, optimisation and simulation models support decisions on emergency ambulance stations’ locations and ambulance management strategies. Essential inputs for such models are the spatiotemporal characteristics of ambulance trips. Access to data on the movements of ambulances is limited, and therefore modelling efforts often rely on assumptions (e.g., the Euclidean distance is used as a surrogate of the ambulance travel time; the closest available ambulance is dispatched to a call; or the travel time estimates, offered by application programming interfaces for ordinary vehicles, are applied to ambulances). These simplifying assumptions are often based on incomplete data or common sense without being fully supported by the evidence. Thus, data-driven research to model ambulance trips is required. We investigated a unique dataset of global positioning system-based measurements collected from seventeen emergency ambulances over three years. We enriched the data by exploring external sources and designed a rule-based procedure to extract ambulance trips for emergency cases. Trips were split into training and test sets. The training set was used to develop a series of statistical models that capture the spatiotemporal characteristics of emergency ambulance trips. The models were used to generate synthetic ambulance trips, and those were compared with the test set to decide which models are the most suitable and to evaluate degrees to which they fit the statistical properties of real-world trips. As confirmed by the low values of the Kullback–Leibler divergence (0.004–0.229) and by the Kolmogorov–Smirnov test at the significance level of 0.05, we found a very good fit between the probability distributions of spatiotemporal properties of synthetic and real trips. A reasonable modelling choice is a model where the exponential dependency on the population density is used to locate emergency cases, emergency cases are allocated to hospitals following empirical probabilities, and ambulances are routed using the fastest paths. The models we developed can be used in optimisations and simulations to improve their validity.

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“…In order to evaluate the performance of the above transition methods, some scholars carried out simulation studies and obtained the advantages, disadvantages and applicable conditions of various methods [11][12][13][14][15]. In addition to the classical methods mentioned above, some scholars used prewritten transition algorithms to control signal transition process [16][17][18]. Other scholars studied this problem by establishing optimization models.…”

Section: Introductionmentioning

confidence: 99%

Control Strategy of Signal Transition after Emergency Vehicle Signal Preemption

Liu

Song

et al. 2020

Discrete Dynamics in Nature and Society

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Emergency vehicle (EV) plays an important role in evacuations or rescues when emergencies occur. To insure that an EV can transfer people in danger to emergency shelters or medical assistance organizations as soon as possible, EV signal preemption (EVSP) strategy is usually adopted. After EV has passed through the intersection, traffic signal has to transfer back to normal signal timing scheme. This paper focuses on the control strategy of EV signal transitioning from EVSP back to normal operation. Considering both efficiency and fairness, the maximum vehicles passing through in per unit time during the transition period and the minimum difference between the maximum and the minimum queue length after transition are selected as objectives, and a multi-objective optimization model is presented. A nondominated sorting genetic algorithm II (NSGA-II) is designed to solve the optimization model and unique encoding and decoding methods are presented. The established model and designed algorithm are verified and the control effect is analyzed. Simulation results indicate that by adopting the control strategy obtained by the presented model, the number of vehicles passing through in per unit time during the transition period is increased and the difference of vehicle length in different directions is reduced significantly, from which we can conclude that the control method proposed in this paper has good performance.

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Analyzing travel time reliability and its influential factors of emergency vehicles with generalized extreme value theory

Cited by 13 publications

References 30 publications

Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia

Control Strategy of Signal Transition after Emergency Vehicle Signal Preemption

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