Travel Time Model Research Articles

Identification and ranking of subaerial volcanic tsunami hazard sources in Southeast Asia

Abstract. Tsunamis caused by large volcanic eruptions and flanks collapsing into the sea are major hazards for nearby coastal regions. They often occur with little precursory activity and are thus challenging to detect in a timely manner. This makes the pre-emptive identification of volcanoes prone to causing tsunamis particularly important, as it allows for better hazard assessment and denser monitoring in these areas. Here, we present a catalogue of potentially tsunamigenic volcanoes in Southeast Asia and rank these volcanoes by their tsunami hazard. The ranking is based on a multicriteria decision analysis (MCDA) composed of five individually weighted factors impacting flank stability and tsunami hazard. The data are sourced from geological databases, remote sensing data, historical volcano-induced tsunami records, and our topographic analyses, mainly considering the eruptive and tsunami history, elevation relative to the distance from the sea, flank steepness, hydrothermal alteration, and vegetation coverage. Out of 131 analysed volcanoes, we found 19 with particularly high tsunamigenic hazard potential in Indonesia (Anak Krakatau, Batu Tara, Iliwerung, Gamalama, Sangeang Api, Karangetang, Sirung, Wetar, Nila, Ruang, Serua) and Papua New Guinea (Kadovar, Ritter Island, Rabaul, Manam, Langila, Ulawun, Bam) but also in the Philippines (Didicas). While some of these volcanoes, such as Anak Krakatau, are well known for their deadly tsunamis, many others on this list are lesser known and monitored. We further performed tsunami travel time modelling on these high-hazard volcanoes, which indicates that future events could affect large coastal areas in a short time. This highlights the importance of individual tsunami hazard assessment for these volcanoes, the importance of dedicated volcanological monitoring, and the need for increased preparedness on the potentially affected coasts.

A Real-Time Prediction Model for Individual Vehicle Travel Time on an Undersaturated Signalized Arterial Roadway

Predicting the segment travel time of a vehicle, which includes its travel time on a road segment and the delay for passing the downstream intersection, can greatly help the drivers to better control a vehicle’s speed to reduce signal delay and gasoline consumption. However, so far, few effective methods have been developed that can accurately characterize an individual vehicle’s segment travel time. To address this problem, this article seeks to develop an analytical model to predict each vehicle’s travel time on a signalized arterial roadway under nonsaturated traffic conditions. The proposed model is developed based on the following three situations a vehicle can experience when passing through a signalized intersection: 1) passing directly in the green time phase, 2) decelerating until coming to a complete stop in the red intervals, and 3) decelerating initially, but passing through without a stop. The situation that a vehicle will encounter is predicted by analyzing the correlation between a vehicle’s entry time to the segment and the signal-phase timing using the kinematic wave theory. Then, the intersection delay of each vehicle is predicted based on the queue shockwave speed and the discharge shockwave speed. A vehicle’s travel time on a road segment is predicted by summing the intersection delay and the segment free-flow travel time. The numerical application shows that the proposed model can accurately characterize a vehicle’s travel time. Thereby, it can be applied in a connected environment to predict the segment travel time to improve the mobility of the traffic.

Distance is “a big problem”: a geographic analysis of reported and modelled proximity to maternal health services in Ghana

BackgroundGeographic barriers to healthcare are associated with adverse maternal health outcomes. Modelling travel times using georeferenced data is becoming common in quantifying physical access. Multiple Demographic and Health Surveys ask women about distance-related problems accessing healthcare, but responses have not been evaluated against modelled travel times. This cross-sectional study aims to compare reported and modelled distance by socio-demographic characteristics and evaluate their relationship with skilled birth attendance. Also, we assess the socio-demographic factors associated with self-reported distance problems in accessing healthcare.MethodsDistance problems and socio-demographic characteristics reported by 2210 women via the 2017 Ghana Maternal Health Survey were included in analysis. Geospatial methods were used to model travel time to the nearest health facility using roads, rivers, land cover, travel speeds, cluster locations and health facility locations. Logistic regressions were used to predict skilled birth attendance and self-reported distance problems.ResultsWomen reporting distance challenges accessing healthcare had significantly longer travel times to the nearest health facility. Poverty significantly increased the odds of reporting challenges with distance. In contrast, living in urban areas and being registered with health insurance reduced the odds of reporting distance challenges. Women with a skilled attendant at birth, four or more skilled antenatal appointments and timely skilled postnatal care had shorter travel times to the nearest health facility. Generally, less educated, poor, rural women registered with health insurance had longer travel times to their nearest health facility. After adjusting for socio-demographic characteristics, the following factors increased the odds of skilled birth attendance: wealth, health insurance, higher education, living in urban areas, and completing four or more antenatal care appointments.ConclusionStudies relying on modelled travel times to nearest facility should recognise the differential impact of geographic access to healthcare on poor rural women. Physical access to maternal health care should be scaled up in rural areas and utilisation increased by improving livelihoods.

Study on Peak Travel Avoidance Behavior of Car Travelers during Holidays

Traveling during off-peak season can mean cheaper flights, cheaper hotels, and the chance to see a destination at a less frenetic time of year. To alleviate the congestion of roads and tourist attractions, a better demand management plan is needed to guide tourists to avoid travel during holidays. This study takes holiday tourists’ peak travel avoidance behavior as the research object, and a Nested Logit (NL) model of travel time and destination joint decisions was established based on Utility Maximization Theory. Model calibration and elastic analysis were carried out using Revealed Preference/Stated Preference (RP/SP) survey data. Results show that tourist attributes such as the number of tourists traveling together, travel companion, duration of the visit, the number of previous visits, tourism motivation, type of tourist attraction, quality grade of tourist attraction, and degree of congestion significantly influence destination decisions. Travel scope, travel duration, age, and other factors significantly influence travel time decisions. The traffic congestion around tourist attractions, holiday admission ticket prices, and non-holiday admission ticket prices significantly influence travel time and destination decisions. Holiday admission ticket price increases have a strong impact on the decision to change the travel destination, while non-holiday admission ticket discounts have a weak impact on travel time decision behavior. The findings of this study offer a theoretical basis for holiday travel management and tourism management. It is practical and significant to reasonably guide tourists to travel during the off-peak season and to understand the travel needs and characteristics of holiday tourists, thus adjusting the distribution of holiday tourist flow.

Travel time model for multi-deep automated storage and retrieval systems with different storage strategies

Travel time models for automated storage and retrieval systems (AS/RS) are used to define average travel times during storage/retrieval operations in an AS/RS. With an increasing depth of AS/RS racks, storage goods are stored in front of each other. This can lead to relocation operations of blocking goods causing higher travel times. This paper derives analytically and presents four travel time models for multi-deep AS/RS following four storage allocation strategies. Two models handle random strategies, one minimises the variance of storage channel fillings and the fourth maximises this variance. Evaluation and comparison of different models is followed by a discrete event simulation to verify these models. It is shown that the minimal variance strategy achieves the lowest relocation numbers and also the lowest total travel times, the random strategies perform between the minimal and maximal variance strategy.

High-Resolution, Quantitative Signal Subspace Imaging for Synthetic Aperture Radar

We consider synthetic aperture radar imaging of a region containing point-like targets. Measurements are the set of frequency responses to scattering by the targets taken over a collection of individual spatial locations along the flight path making up the synthetic aperture. Because signal subspace imaging methods do not work on these measurements directly, we rearrange the frequency response at each spatial location using the Prony method and obtain a matrix that is suitable for these methods. We arrange the set of these Prony matrices as one block-diagonal matrix and introduce a signal subspace imaging method for it. We show that this signal subspace method yields high-resolution and quantitative images provided that the signal-to-noise ratio is sufficiently high. We give a resolution analysis for this imaging method and validate this theory using numerical simulations. Additionally, we show that this imaging method is stable to random perturbations to the travel times and validate this theory with numerical simulations using the random travel time model for random media.

Integrated ANN-Bayes-based travel time prediction modeling for signalized corridors with probe data acquisition paradigm

Travel time is a major information in support of Transportation System Management and Operations (TSMO). Accurate travel time information extracted from probe data sources, which have been evolved in recently years, has led to the considerable research into travel time prediction modeling on freeways. However, little research has been reported on dealing with predicting signalized arterial travel time. It is mainly because of the natures of interrupted traffic flows along a signalized corridor or dynamic urban network, wherein varying signal timing schemes, crossing traffic, and dynamic route choices make arterial travel time estimations much more challenging than freeways. This paper presents an innovative methodological framework that can be used to integrate the exponential smoothing technique, Artificial Neural Network (ANN) techniques and Bayes algorithms for predicting travel time along a signalized corridor. The data source obtained along the US-27 corridor in the State of Ohio, was used to test two types of the developed models, i.e., hourly instantaneous travel time prediction model and realized (or experienced) route travel time prediction model. The study results indicate that the developed models can effectively capture the travel time patterns by combining an exponential smoothing base profile and an ANN-Bayes-trained dynamic profile. In this way, more sensitive changes can be identified to a variation aroused by non-recurring congestion. The testing results with real-world travel time datasets indicates a good performance of the predicted travel time models. The most errors range from −0.608 to 0.485, which are much lower than the threshold standard deviation if 2.24 and close to 6% of mean travel time value of 9.08 min. Overall, the study results suggest that the proposed methods are capable of capturing traffic patterns even when an incident occurs by comparing with conventional methods.

Adaptive large neighborhood search for the time-dependent profitable dial-a-ride problem

This paper is motivated by a non-emergency ambulance transportation service provider that picks up and drops off patients while considering both the time window for medical appointments and the maximum ride-time constraint for each patient. Varying travel times based on departure times further complicates the feasibility evaluation of a given route under both constraints. This problem aims to maximize the net profit which is calculated as the collected reward of serving the selected requests minus the total travel cost of the designed route. The problem is modeled as a time-dependent profitable dial-a-ride problem (TD-PDARP) with a single-vehicle using a mixed-integer linear programming (MILP) model. We propose a tailored feasibility evaluation procedure to handle the complicated maximum ride-time constraint under the time-dependent travel time model, which is then embedded in a hybrid algorithm to solve the proposed problem. This hybrid algorithm leverages an adaptive large neighborhood search (ALNS) for large-scale exploration together with local search (LS) techniques to exploit local regions comprehensively. We evaluate the performance of the proposed algorithm on newly generated TD-PDARP instances. The experiments show that our ALNS-LS algorithm can solve large instances that cannot be solved by commercial solvers in a reasonable time. Furthermore, for all instances that can be solved by the solver within 12 h, our proposed heuristic algorithm is able to obtain the optimal solutions and takes only 1.03% of the average run time required by the solver.

Performance evaluation of a robotic mobile fulfillment system with multiple picking stations under zoning policy

This paper evaluates the performance of a robotic mobile fulfillment system (RMFS) with multiple picking stations under a zoning policy. Focusing on a single picking process performed by robot, we develop an expected travel time model for the robot considering a constant velocity, and evaluate the system performance according to the travel time. Compared with the data of the actual RMF system from FAW Logistics Co., Ltd, the model developed in this study has high accuracy and can be used to rapidly evaluate the performance of RMFS with different layouts. In addition, by the numerical experiments, the effect of the number of horizontal aisles and two zoning policies on system performance is analyzed. The results show the following: (1) With the initial total number of storage locations determined, when picking efficiency and space utilization are taken as indexes for planning and designing, the optimal scheme can be obtained more directly by focusing on the effect of the number of horizontal aisles on the expected travel time. (2) Under the relevant assumptions and system characteristics proposed herein, when the expected travel time is taken as the index, it is always better to store A-class goods in the center of the pod storage area.

Comparing modelled with self-reported travel time and the used versus the nearest facility: modelling geographic accessibility to family planning outlets in Kenya

IntroductionGeographic access to family planning (FP) services has been characterised through a variety of proximity metrics. However, there is little evidence on the validity of women’s self-reported compared with modelled...

On the interplay between hillslope and drainage network flow dynamics in the catchment travel time distribution

AbstractTravel time is a robust measure of water transport dynamics in catchments. At a given control section along a drainage network, travel time distribution results from an interplay between two main processes: (i) the transport through the hillslopes and (ii) transport through the drainage network. The main scope of this work is to quantify this interplay, specifically we aim to identify the relative impact of hillslopes and channels on the travel time at the catchment scale. A theoretical framework is developed following a bottom‐up modelling approach that combines a Boussinesq model for water flow and water travel time in hillslopes, with a geomorphological model for water transport in drainage network. Simple semi‐analytical solutions are derived for the first two moments of the travel time distribution within a flow section. We provide some relevant examples based on synthetic rainfall data, exploring the relative impacts of hillslope and channel properties. As expected, the dynamics of the hillslopes control the travel time distribution at the catchment scale. The drainage network typically introduces a lag in the average travel time exiting the hillslopes and reduces the temporal fluctuations of the mean travel time and its variance. Our theoretical model provides meaningful insights on the investigation of the dominant dynamics taking place in catchments: results suggest that hillslopes and their features are the main driver of travel time in catchments. The temporal fluctuations of the travel time moments show a non‐linear dependence with the recharge time‐series and need to be considered as time‐variant. Variability of water ages collected in a single water sample can be very large, the latter feature may have a significant effect on the water quality and on the tracer data analysis, largely governed by the contact times between water and catchment material.

The selection of income measures in value of travel time models and their implications for the VTT, its cross-sectional income elasticity and transport appraisal

This paper investigates the extent to which the selection of income variables during the estimation of choice models underlying national value of time (VTT) studies affects the cross-sectional income elasticity and the resulting VTT. Using data from the most recent GB (Great Britain) national VTT study, we focus on two contentious issues in relation to the income variable, namely taxation and within household budget allocation. Our work finds that accounting for the progressive nature of income tax and social benefits both increase the cross-sectional income elasticity. In the same vein, assumptions regarding the within-household budget allocation affect the cross-sectional income elasticity. Although the cross-sectional income elasticity is sensitive to the selected income variable, the impact on the nationally representative VTT is insignificant if the stated preference sample is representative with respect to the income variable. The observed discrepancies between the cross-sectional and the inter-temporal income elasticity of the VTT highlight that using these elasticities interchangeably influences the evaluation of future transport infrastructure decisions in terms of value for money.

A Spatial Analysis of Dispersion Mechanisms in the Hydrological Response Using a Spatially Distributed Travel Time Model

AbstractVarious dispersion mechanisms are known to link the spatial heterogeneity of the basin morphology and flow dynamics to the hydrologic response. Recent studies have conducted spatial analysis of the kinematic and geomorphological dispersion coefficients that characterize these mechanisms, as well as identified the influence of hillslopes, channels, and runoff production on their values. Nevertheless, an explicit and distributed dynamic representation of the travel times has not been used for this purpose. This study quantifies dispersion mechanisms by calculating spatially distributed flood wave travel times and hillslope and channel velocities, with a spatially distributed kinematic‐wave based model that explicitly accounts for the path heterogeneity, the dynamics of upstream flow contributions, and the nonlinear dependence on runoff intensity. Hence, the role of these factors and their influence on the dispersion mechanisms across different scales is studied in detail. Using different runoff intensities, the scaling of the dispersion mechanism coefficients according to the contributing areas within the Quintero Creek basin and two of its sub‐basins (in central Chile) is obtained. These scaling relationships are compared against those previously reported in the literature, which were obtained with approaches that simplify the treatment of travel times. Both basin‐specific and more general factors manifesting in the behavior of the dispersion mechanisms are found. Dispersion coefficients increase with area, and the role of the hillslope‐channel transition is controlled by runoff intensity, affecting the relative contribution of the dispersion mechanisms to the total dispersion at different spatial scales.

Modelling of segment level travel time on urban roadway arterials using floating vehicle and GPS probe data

With the increasing traffic congestion levels on urban arterials, an essential step to tackling this challenge is to effectively quantify it and understand how it relates to its contributing factors. Although researchers have proposed models that relate travel time with several traffic and roadway factors for quantifying congestion especially in the Western world, most of these models lack the predictive accuracy for arterials in low-income countries due to differences in roadway and roadside interference factors, heterogeneous traffic flow, and others. A number of them do not incorporate delays from factors including on-street parking activities. Additionally, some existing models are complex in structure and require several parameters which may not be available in many low-income countries such as Ghana or may be expensive to collect. This study aimed at exploring a simple model for predicting travel time which will capture the contributing factors of congestion typical of low-income country arterial road environment and flow characteristics using a multiple linear regression model. Using moving observer method, traffic and roadway data were collected from eight arterial roadways in the Greater Kumasi Metropolitan Area for modeling travel time at the segment level. The fitted model that captures the impact of factors including on-street parking, access density, traffic density, and segment downstream bounding conditions on travel time will aid decision making by transport planners on the factors to consider to mitigate congestion. The model demonstrates how inadequate enforcement of on-street parking restrictions on arterial roadways exacerbates congestion during periods of high demand.

Upwind, No More: Flexible Traveltime Solutions Using Physics-Informed Neural Networks

The eikonal equation plays an important role across multidisciplinary branches of science and engineering. In geophysics, the eikonal equation, and its characteristics, are used in addressing two fundamental questions pertaining to seismic waves: what paths do the seismic waves take (its spreading)? and how long do they take? There have been numerous attempts to solve the eikonal equation, which can be broadly categorized as finite-difference and physics informed neural network (PINN) based approaches. While the former has been developed and optimized over the years, it still inherits some numerical inaccuracies and also the cost scales exponentially with the velocity model size. More importantly, it requires upwind calculations to satisfy the viscosity solution. PINNs, on the other hand, have shown great promise due to several features allowing for higher accuracy and scalability than conventional approaches. In this paper, we demonstrate another unique feature of PINN solutions, specifically its flexibility resulting from the global nature of its NN functional optimization, allowing for functional gradients referred to as automatic differentiation. This feature allows us to overcome the inability of conventional methods to handle large areas of missing information (gap) in the velocity model. We find empirically that the PINNs interpolation-extrapolation inherent capability enables us to circumvent a scenario when traveltime modelling is performed on velocity models containing gaps. Such a capability is crucial when performing traveltime modelling using the global tomographic Earth velocity model.

Accessibility analysis of battery electric vehicles: A case study of Beijing Tianjin Hebei

By considering the symbiosis between rest behavior with charging behavior during intercity traveling, the inconvenience of battery electric vehicles (BEVs) confined by the battery capacity can be relieved. This paper proposed a quantitative analysis method for revealing this phenomenon from an accessibility perspective. This proposed method involved a calculation model of intercity travel time for electric vehicle by considering the current configuration of electric vehicle charging facilities and energy consumption with travel speed. Based on the highway network and charging pile network in Beijing-Tianjin-Hebei region, a case study has been analyzed to verify the feasibility of the model. The results show that the accessibility performance of BEVs and fuel vehicles is close in Beijing Tianjin Hebei region, which has relatively perfect charging facilities. And by joint the charging behavior and rest behavior, there is almost no difference between BEV models with a large range and advanced charging technology and fuel vehicles in intercity travel. In addition, the charging facilities along the expressway in the region can basically satisfy the experience of intercity travel for BEVs, and further increasing the number of charging stations cannot significantly improve the accessibility of electric vehicles.

Topography-dependent eikonal tomography based on the fast-sweeping scheme and the adjoint-state technique

First-arrival traveltime tomography has been widely used for upper crustal velocity modeling, but it usually suffers from the problem of complex surface topography. To overcome this problem, we have developed a new topography-dependent eikonal tomography scheme that combines a developed accurate and efficient traveltime modeling method and introduces a flexible and robust adjoint inversion scheme in the presence of irregular topography. A surface-flattening scheme is used to handle the irregular surface, where the real model is discretized by curvilinear grids and the irregular free surface is mathematically flattened through the transformation from Cartesian to curvilinear coordinates. Based on this parameterization, the forward traveltime modeling is conducted by a monotone fast-sweeping method that discretizes the factored topography-dependent eikonal equation with a point-source condition. This algorithm can circumvent the source-singularity problem and decrease the numerical error in the vicinity of a point source in the curvilinear system. Then, the gradient-based inversion is used to minimize the misfit function, which is achieved by a matrix-free adjoint-state method without cumbersome ray tracing and explicit estimation of the Fréchet derivative matrix in the curvilinear coordinate system. The new tomographic scheme is evaluated through numerical examples with different seismic structures with complex topography, and then applied to a wide-angle profile acquired in the northeastern Tibetan Plateau. The results validate the effectiveness and efficiency of our tomography scheme in constructing shallow crustal velocity models with irregular topography.

Modeling of urban road network traffic carrying capacity based on equivalent traffic flow

The determination of the traffic carrying capacity of road network is of great significance to the scale control of urban vehicles and the control of transportation systems. Existing related works have proposed calculation models and solutions for different application environments. However, these studies have problems such as diversification of capacity definitions, incorrect analysis of factors, and excessive model constraints. In response to these problems, this paper aims to determine the connotation of traffic carrying capacity and put forward a novel calculation model for carrying capacity. Firstly, in-depth analysis found that the key factor affecting the number of vehicles is the service level of road network. Then, the connotation of traffic carrying capacity is clarified, that is, the maximum number of vehicles travelling simultaneously in the road network under a certain service level. Second, based on the definition of carrying capacity and the characterization relationship between average travel speed and service level, the calculation of carrying capacity is transformed into the establishment of relationship between average travel speed and maximum number of vehicles. The construction of this relationship includes five steps, which are proposal of equivalent lane, division of traffic basic units, construction of basic unit travel time model, calculation of basic unit carrying capacity, and calculation of road network carrying capacity. In particular, the proposal of equivalent lane achieves the conversion from intermittent flow to continuous flow, and the abstraction of traffic flow further unifies the operation process into a unified mode of free travel and stacked release. Finally, the VISSIM simulation software is employed to verify and evaluate the proposed models. The results show that the average relative error between the simulated data and the calculated data obtained from the model is -0. 46%. Findings from this study will provide an innovative idea for the evaluation of urban road network performance. It can also provide a basis for urban managers to improve service level of road network and optimize the design of transportation system.

Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling

The popularization of electric vehicles faces problems such as difficulty in charging, difficulty in selecting fast charging locations, and comprehensive consideration of multiple factors and vehicle interactions. With the increasingly mature application of navigation technology in vehicle-road coordination and other aspects, the proposal of an optimal dynamic charging method for electric fleets based on adaptive learning makes it possible for edge computing to process electric fleets to effectively execute the optimal route charging plan. We propose a method of electric vehicle charging service scheduling based on reinforcement learning. First, an intelligent transportation system is proposed, and on this basis a framework for the interaction between fast charging stations and electric vehicles is established. Subsequently, a dynamic travel time model for traffic sections was established. Based on the habits of electric vehicle owners, an electric vehicle charging navigation model and a reinforcement learning reward model were proposed. Finally, an electric vehicle charging navigation scheduling method is proposed to optimize the service resources of the fast charging stations in the area. The simulation results show that the method balances the charging load between stations, can effectively improve the charging efficiency of electric vehicles, and increases user satisfaction.

Analysis of pickup/deposit stations and dwell point locations in bi-directional flow-rack AS/RSs

A traditional flow-rack automated storage and retrieval system stores unit-loads in storage lanes with the same slope. This paper considers a new bi-directional flow-rack automated storage and retrieval system, in which adjacent storage lanes slope to opposite directions. While such a system can conduct a dual-command cycle, it is critical to operate the system in an efficient way. We focus on both the pickup/deposit (P/D) station policy and the dwell point location policy, considering two scenarios for each policy. We first build a single-command and a dual-command travel time models for each examined P/D station policy and dwell point location policy. Then, we validate the accuracy of the analytical models by simulation, and theoretically compare the examined P/D point policies and dwell point location policies, in terms of both single-command and dual-command cycle times. We find that locating both the P/D station and the dwell point location at the midpoint of the rack is the best policy. We prove the advantage of the BFR AS/RSs over the traditional flow-rack AS/RSs in terms of single-command cycle time, and that of the dual-command cycle over the single-command cycle. Two case studies are also conducted to examine the effectiveness of the examined policies.