Actual Travel Time Research Articles

Transit Time Distributions and Ventilation Pathways Using CFCs and Lagrangian Backtracking in the South Atlantic of an Eddying Ocean Model

Abstract Transit time distributions (TTDs) for the Antarctic Intermediate Water (AAIW) in the South Atlantic Ocean are estimated from an eddying ocean model with a twofold scope: validation of the TTD method and identifying pathways of the AAIW. The TTDs are inferred both from Lagrangian particle backtracking and the modeled CFC-11 concentrations, under the assumption that the TTDs can be described with an inverse Gaussian function. A bimodal distribution is obtained for the Lagrangian TTDs with four major subduction regions identified: near the Agulhas retroflection, south of New Zealand, west of the Drake Passage (smallest mean age Γ = 13 years), and in the Argentine basin (largest mean age Γ = 25 years). With the Southern Ocean as source region, the inverse Gaussian is a reasonable representation for the TTDs in the eastern Atlantic basin (40°–35°S, 0°–20°E), whereas the fit for region west (40°–35°S, 60°–40°W) of the mid-Atlantic ridge is not as good and overestimates the TTDs for transit times < 15 years. Mean ages from the modeled CFC-11 are mostly larger (up to 12 years) in the eastern Atlantic basin, and they are mostly smaller than the Lagrangian mean ages in the west. Both methods yield mean ages smaller in the western than in the eastern Atlantic basin and an aging of AAIW from the 1990s to the 2000s that is consistent with reduced flow velocities. The Antarctic Circumpolar Current appears to be the prime determinant of the transit times. The results suggest that the inverse Gaussian, despite assuming 1D advection–diffusion with constant mean flow and diffusivity, is a surprisingly good fit. Significance Statement In this article, we assess the transit time distribution method, often used to estimate anthropogenic carbon uptake in the ocean from observations, thereby exploring particle pathways from the surface into the ocean interior in the South Atlantic Ocean. We track thousands of particles in a model from their point of origin near the surface to the ocean interior. The tracking reveals multiple routes and gives the actual travel time of these particles, which we compare with the travel times predicted by theory. Thus, this research deepens our understanding of the routes and travel times of the water particles, which is important for the ocean circulation, and provides insights to improve the methods to infer anthropogenic carbon uptake, storage, and transport.

Scaling of spatio-temporal variations of taxi travel routes

The importance of understanding human mobility patterns has led many studies to examine their spatial-temporal scaling laws. These studies mainly reveal that human travel can be highly non-homogeneous with power-law scaling distributions of distances and times. However, investigating and quantifying the extent of variability in time and space when traveling the same air distance has not been addressed so far. Using taxi data from five large cities, we focus on several novel measures of distance and time to explore the spatio-temporal variations of taxi travel routes relative to their typical routes during peak and nonpeak periods. To compare all trips using a single measure, we calculate the distributions of the ratios between actual travel distances and the average travel distance as well as between actual travel times and the average travel time for all origin destinations during peak and nonpeak periods. In this way, we measure the scaling of the distribution of all single trip paths with respect to their mean trip path. Our results surprisingly demonstrate very broad distributions for both the distance ratio and time ratio, characterized by a long-tail power-law distribution. Moreover, all analyzed cities have larger exponents in peak hours than in nonpeak hours. We suggest that the interesting results of shorter trip lengths and times, characterized by larger exponents during rush hours, are due to the higher availability of travelers during rush hours. Thus, drivers are more motivated to shorten their trips in order to take new passengers in rush hours compared to non-rush hours. We also find a high correlation between distances and times, and the correlation is lower during peak hours than during nonpeak hours. The reduced correlations can be understood as follows. Due to the high availability of passengers in peak periods more drivers choose long distances to save time compared to nonpeak periods. Furthermore, we employed an indeterminate traffic assignment model, which supports our finding of the power-law distribution of the distance ratio and time ratio for human mobility. Our results can help to assess traffic conditions within cities and provide guidance for urban traffic management.

Simulating multiple urban land use changes by integrating transportation accessibility and a vector-based cellular automata: a case study on city of Toronto

ABSTRACT The accessibility provided by the transportation system plays an essential role in driving urban growth and urban functional land use changes. Conventional studies on land use simulation usually simplified the accessibility as proximities and adopted the grid-based simulation strategy, leading to the insufficiencies of characterizing spatial geometry of land parcels and simulating subtle land use changes among urban functional types. To overcome these limitations, an Accessibility-interacted Vector-based Cellular Automata (A-VCA) model was proposed for the better simulation of realistic land use change among different urban functional types. The accessibility at both local and zonal scales derived from actual travel time data was considered as a key driver of fine-scale urban land use changes and was integrated into the vector-based CA simulation process. The proposed A-VCA model was tested through the simulation of urban land use changes in the City of Toronto, Canada, during 2012–2016. A vector-based CA without considering the driving factor of accessibility (VCA) and a popular grid-based CA model (Future Land Use Simulation, FLUS) were also implemented for comparisons. The simulation results reveal that the proposed A-VCA model is capable of simulating fine-scale urban land use changes with satisfactory accuracy and good morphological feature (kappa = 0.907, figure of merit = 0.283, and cumulative producer’s accuracy = 72.83% ± 1.535%). The comparison also shows significant outperformance of the A-VCA model against the VCA and FLUS models, suggesting the effectiveness of the accessibility-interactive mechanism and vector-based simulation strategy. The proposed model provides new tools for a better simulation of fine-scale land use changes and can be used in assisting the formulation of urban and transportation planning.

Perceived and actual travel times in a multi-modal urban public transport network: comparing survey and AVL data

Perceived travel times of travelers are usually longer than actually realized travel times, implying that passengers’ experience of travel time savings is different from objectively calculated savings. This study provides additional empirical evidence on this topic, by comparing the passengers’ perceived travel times reported in an (online) survey with their corresponding actual in-vehicle travel times from Automatic Vehicle Location (AVL) data. The case study involves the metro, tram and bus network of Amsterdam, the Netherlands. On average, travelers perceive their travel time to be 1.9 min (11%) longer than their actual realized travel time. The perceived values match the scheduled values slightly better than the actually realized values. Furthermore, we found a larger travel time over-perception for metro compared to tram and bus. This is a counter-intuitive result, since the metro has been found to have a less negative travel time perception than busses in the public transport choice modelling literature. When the travel purpose is considered, the leisure time purposes recreation and shopping have a significantly smaller travel time over-perception than work-related journeys. Opening a new metro line did not have a significant influence on the travel time perception of travelers in Amsterdam.

Research on the Intelligent Assignment Model of Urban Traffic Planning Based on Optimal Path Optimization Algorithm

Congestion and complexity in the field of highway transportation have risen steadily in recent years, particularly because the growth rate of vehicles has far outpaced the growth rate of roads and other transportation facilities. To ensure smooth traffic, reduce traffic congestion, improve road safety, and reduce the negative impact of air pollution on the environment, an increasing number of traffic management departments are turning to new scientifically developed technology. The urban road traffic is simulated by nodes and sidelines in this study, which is combined with graph theory, and the information of real-time changes of road traffic is added to display and calculate the relevant data and parameters in the road. On this foundation, the dynamic path optimization algorithm model is discussed in the context of high informationization. Although the improved algorithm’s optimal path may not be the conventional shortest path, its actual travel time is the shortest, which is more in line with users’ actual travel needs to a large extent.

A stochastic approach for planning airport ground support resources

AbstractAirport ground operations are one of the main causes of late departures. Hence, reliable work plans for ground support resources can help mitigate these delays while limiting the under‐/over‐utilization of equipment. In this paper, we propose a stochastic approach for modeling the routing problems encountered during scheduling of each activity. We embed Monte Carlo simulation within an optimization approach to assess solutions during the search and guide the algorithm toward more robust schedules that account for resource cost. Actual flight arrival data, random service, and travel times are used in the simulation process. Results prove that our method clearly increases the robustness of the obtained schedules and the number of aircraft being handled within the planned time. To further identify potential causes of delay, we propose a reliability analysis based on the study of survival functions for each turnaround activity, combined with the dispersion of the resources' arrival to the parking stand.

Analysis of Route Choice During Planned and Unplanned Road Closures

The Federal Highway Administration (FHWA) Alternate Route Handbook proposes guidance to identify alternate routes during planned and unplanned road closures. A challenge with this process is the lack of traffic data available to decision-makers. High volume corridors experiencing unplanned closures can provide a rich case history by systematically collecting connected vehicle (CV) data during such incidents. CV data provide the ability to directly measure actual diversion routes and travel times during an ongoing or historical incident. This paper presents methodologies to systematically analyze diversion data to identify the most common alternate route choices and impacted interstate exits, valuable information for public safety and transportation agencies to evaluate the surrounding road network’s resiliency in accommodating diverting traffic. Agencies can use this information to proactively deploy resources (officers, signs, barricades) at critical locations during future closures. The scalability of this methodology is demonstrated by evaluating 12 additional cases to assess diversion rates found to be in the range of 58% to 93% for total closures exceeding five hours. The paper concludes by recommending agencies apply these methodologies to develop data-driven diversion strategies on critical routes coupled with real-time CV monitoring in dispatch centers to provide agile adjustment of resources along diversion routes.

Time Savings vs. Access-Based Benefit Assessment of New York’s Second Avenue Subway

AbstractUnder the current practice of benefit-cost analysis, the direct economic benefits produced by a newly built transit facility are assessed based on how it affects travel time and various costs that are associated with transport needs and travel behavior. However, the time-saving-based benefit calculation approach has been questioned and criticized. Given the strong correlation between accessibility and land value, we propose the access-based land value benefit assessment as an alternative, and apply this assessment method to analyzing the Second Avenue Subway project in Manhattan, New York. The primary principle of the access-based method is that the economic value of a transport project’s intangible gains is largely capitalized by nearby properties’ value appreciation, which is directly caused by improved transport accessibility. We find that: (i) the actual travel time saving is lower than originally forecast; (ii) a strong positive correlation between residential property value and job accessibility by transit is observed; (iii) the appreciation in sold property value and rented property value both far exceed total project cost; and (iv) such results support the decision to approve and construct the Second Avenue Subway.

An extension of the gravity model

We introduce a practically useful extension of the gravity model. When specifying the distance decay function, the basic gravity model and its variants use actual distance or travel time. But, in reality, travel time to a retail outlet is only a fraction of the time spent on shopping trips. Hence, ignoring “time spent in facilities” may bias market share estimates in a competitive environment. To address this issue and by-passing the need to know time spent on facilities, we propose a simple extension of the gravity model by introducing an extra distance/travel time parameter. For its implementation, zip code (or neighborhood) information of intercepted patrons at facilities is sufficient to determine the extra distance making it practically appealing. To illustrate the estimation and also empirically validate the proposed approach, we analyze a shopping malls application.

Origin-Aware Location Prediction Based on Historical Vehicle Trajectories

Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Nevertheless, due to the time and space complexity, these methods (e.g., Markov models) only utilize the just passed locations to predict next locations, neglecting earlier passed locations in the trajectory. In this work, we seek to enhance the prediction performance by incorporating the travel time from all the passed locations in the query trajectory to each candidate next location. To this end, we propose a novel prediction method, namely the Travel Time Difference Model, which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Moreover, we integrate the Travel Time Difference Model with a Sequential and Temporal Predictor to yield a joint model. The joint prediction model integrates local sequential transitions, temporal regularity, and global travel time information in the trajectory for the next location prediction problem. We have conducted extensive experiments on two real-world datasets: the vehicle passage record data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over baseline methods.

Associations between the sense of accessibility, accessibility to specific destinations, and personal factors: A cross-sectional study in Sendai, Japan

Good urban and transport policymaking requires understanding both calculated/perceived accessibility to specific destinations and sense of accessibility (residents’ overall feelings of (in)convenience in their daily lives). This study investigated the correlations between personal factors, calculated accessibility (actual distances and travel time), perceived accessibility (residents’ perceived possibility of accessing specific destinations), and sense of accessibility. We conducted an online survey involving residents in the Sendai metropolitan area, Japan, from January 15 to February 9, 2021. The sample size was 1474. We considered previous studies and accessibility-related problems in Japan to determine what destinations and personal attributes the survey would enquire. The estimation results of ordinary regression models demonstrated that personal factors, including self-rated health, living in a central city, household composition, and transportation usage were associated with several measures of sense of accessibility. Good calculated accessibility to stations and food retailers was positively associated with the sense of accessibility without cars. Non-commuting and commuting longer than 30 min were positively associated with the sense of accessibility to what the respondents wanted. Perceived accessibility to important daily life destinations was positively associated with sense of accessibility. The neighborhood social cohesion was partly associated with the current sense of accessibility; however, this association was not observed under the assumption of not having access to a car. The results provide notable findings that connect existing accessibility measures and sense of accessibility in an Asian context.

Modeling earthmoving operations in real time using hybrid fuzzy simulation

Predicting and optimizing performance in earthmoving operations is critical, because they are essential to many construction projects. The complexity of modeling earthmoving operations remains challenging, even with several modeling techniques available, including simulation. This paper advances the state-of-the-art of modeling earthmoving operations by introducing a hybrid fuzzy system dynamics–discrete event simulation framework with the capacity to: capture the dynamism of performance in earthmoving operations; capture subjective uncertainty of several factors affecting them; model their sequential nature and resource constraints; and determine actual travel time, in real time, using online navigation systems. Findings from this research confirm the proposed framework (i) extends the application of simulation techniques for modeling construction processes involving dynamic input variables and subjective uncertainty, through its ability to capture the non-probabilistic uncertainty of construction systems, and (ii) when combined with the use of online navigation systems to assess trucks’ travel time, improves the accuracy of earthmoving operation models.

Developing IT-enabled performance monitoring system for green logistics: a case study

PurposeLogistics companies are compelled to improve their efficiency and the environmental performance by introducing the green concept in their operations. The main purpose of the paper is to have continuous monitoring and tracking of logistics operations to enhance the green performance.Design/methodology/approachThis research uses a case study approach. It illustrates a monitoring system to digitize the logistics activities by sending real-time GPS data to the server and capture the surrounding pictures with the help of the internet of things (IoTs) based camera. Data generated through digitization is mathematically analyzed for ensuring a green logistic system. The alerts due to the halts, help in keeping a check on fuel consumption, carbon emissions and security of logistics. Performance indicators such as carbon emissions and the value of travel time saving (VTTS) are selected for the study.FindingsThe findings of the study show that the actual travel time and distances are higher than the estimated travel time and distances. It also reveals that actual travel routes with diversions involve a considerably higher amount of carbon emissions during all sample travels. The results indicate a considerable saving in terms of carbon emissions, time and cost savings by effective practices of Green Logistics in Monitoring system (MSGL). These findings can help the top management of logistics companies in formulating effective strategies for technology applications in logistic operations to ensure green performance.Research limitations/implicationsThe study has been carried out under certain set of conditions, which may vary depending upon the organizations. Also certain more environmental dimensions for performance monitoring can be used as a future scope of study. Further studies also need to be carried out to widen the scope of the MSGL model at a global level rather than only at local level.Originality/valueAny organization which deploys transportation as part of their activity can use this MSGL model and then do the mathematical analysis to reduce the CO2 emissions, reduce the time and extra cost. The value of this study lies in the fact that government is trying different methods and models to reduce pollution. This MSGL should be made compulsory by the government as a part of their policies for environment of the country.

The potential geographical accessibility to public hospitals for the population in Bucharest’ proximity

Access to health services is essential for ensuring a fundamental human right: the right to health care. Traditional accessibility methods do not consider traffic changes and possible infrastructure works and do not always reflect the actual travel time to the nearest hospital. This study tries to measure the potential access to hospitals of the population from the proximity of Bucharest, an area overlapping Ilfov County, using API (Application Program Interface). Two scenarios were considered. The first represents accessibility in the morning, an interval with heavy traffic, and the second represents accessibility in the evening, an interval without heavy traffic. The results confirm that Ilfov County has good accessibility to public hospitals, with over 60% of the population travelling less than 30 minutes to the nearest hospital. The proximity of Bucharest makes it possible for residents to access hospitals in that area quickly. The findings provide a scientific basis for local authorities to optimize access to hospitals and planning resources.

A modified artificial bee colony algorithm for the dynamic ride-hailing sharing problem

Ride-hailing sharing involves grouping ride-hailing customers with similar trips and time schedules to share the same ride-hailing vehicle to reduce their total travel cost. With the current information and communication technology, ride-hailing customers and drivers can be matched in real-time via a ride-hailing platform. This paper formulates a dynamic ride-hailing sharing problem that simultaneously maximizes the number of served customers, minimizes the travel cost and travel time ratios, and considers the capacity, time window, and travel cost constraints. The travel cost ratio is the ratio of actual passengers’ fare to the passengers’ fare without ride-hailing sharing, whereas the travel time ratio is defined as the actual travel time (including waiting time) over the maximum allowable travel time. To solve the dynamic problem, it is divided into many small and continuous static subproblems with an equal time interval. Each subproblem is solved by a modified artificial bee colony (MABC) algorithm with path relinking, while the contraction hierarchies and vantage point tree are used to determine the shortest path and accelerate the algorithm, respectively. Problem properties and the performance of the proposed solution method are demonstrated using large-scale real-time data from Didi that is the largest ride-hailing company in China. The proposed method is shown to outperform the benchmark, i.e., greedy randomized adaptive search procedure (GRASP) with path relinking. The proposed method also performs better when the length of each time interval is longer, and the tolerance for the incremental travel time caused by detours is higher. We also demonstrate that (a) considering both travel cost and travel time ratios in the objective can achieve a better sharing percentage, and balance the increase in the travel time ratio and the decrease in the travel cost ratio compared with the objective that misses either travel time or the travel cost ratio; and (b) the passengers can gain a large out-of-pocket cost saving in the case of ride-hailing sharing while enduring a relatively small increase in travel time compared with the case without ride-hailing sharing.

Верификация экспериментами моделей скорости движения лесовозов в зависимости от природно-производственных факторов

One of the key production parameters of the forest transport process is the speed of movement of timber carriers when removing wood from the upper warehouse. This speed determines the maximum possible volume of prepared and exported wood. Many natural and industrial factors affect the speed of a timber truck. The speed of transport depends on the traffic conditions on the roads, which are significantly complicated by adverse weather events. Scientists have identified laws in changing the speed modes of moving vehicles based on practical observations of individual road sections. To identify the dependence of the speed of a logging truck on 31 factors under consideration, 162 observations were made on measurements of the speed of logging trucks in various natural and industrial conditions. Mathematical models have been developed for calculating the speed of a logging vehicle, taking into account natural and industrial factors. Verification of the obtained nonlinear and wave regression models is the goal of this article. To verify the models, an additional experiment was carried out to compare the calculated models of speed modes of timber carriers with the actual ones. During the experiment, we used data on the speed of a logging truck when transporting wood from the upper warehouse in the cutting area to the raw material warehouse of a wood processing enterprise on the territory of the Yenisei forest district of the Krasnoyarsk territory. The logging road was divided into six sections, each of which was divided into elementary sections by slopes. At each section, the parameters that affect the speed of the timber carrier were determined using previously identified multi-factor equations, and the estimated time of cargo delivery by the timber carrier to the destination was obtained. Comparing the actual travel time with the calculated one based on previously identified equations for a timber truck with cargo on the way from the upper warehouse to the point of wood delivery, it was found that the estimated time of cargo delivery coincides with the actual one with an error of 7 %, and this is an acceptable norm(less than 30 %) for technological processes. Thus, the obtained results of testing multi-factor models can be considered reliable.

“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

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...

A Multi-Start Granular Skewed Variable Neighborhood Tabu Search for the Roaming Salesman Problem

This paper presents a novel hybrid metaheuristic algorithm for the Roaming Salesman Problem (RSP), called Multi-Start Granular Skewed Variable Neighborhood Tabu Search (MS-GSVNTS). The objective in RSP is to design daily tours for a traveling campaigner who collects rewards from activities in cities during a fixed planning horizon. RSP exhibits a number of exclusive features: It is selective which implies that not every node needs a visit. The rewards of cities are time-dependent. Daily tours can be either an open or a closed tour which implies the absence of a fixed depot. Instead, there is a campaign base that is to be attended frequently. Multiple visits are allowed for certain cities. The proposed method MS-GSVNTS is tested on 45 real-life instances from Turkey which are built with actual travel distances and times and on 10 large scale instances. Computational results suggest that MS-GSVNTS is superior to the existing solution methods developed for RSP. It produces 50 best known solutions including 18 ties and 32 new ones. The performance of MS-GSVNTS can be attributed to its multi-start feature, rich neighborhood structures, skewed moves, and granular neighborhoods.

Hangar Utility Simulation as a Traffic Flow Density Scenario in Giwangan Market Yogyakarta

Giwangan Market as a fruit and vegetable wholesale market needs to be evaluated, organized and improved in the services. Currently, its development has undergone many changes. The hangar, which is the main block for loading and unloading fruits and vegetables, has shifted its function in several places. With the addition of semi-permanent buildings on the sides of hangars, the loading and unloading area are reduced and the entry and exit routes for vehicles are not functioning properly. 86% of vehicles with a tonnage of 2000 - 5000 kg that entered the Giwangan market were in the market for an average of 89.9 minutes. It is because the vehicle is not only loading and unloading but also selling in the market. So that other vehicles cannot load and unload. This study aims to measure the system performance which is estimated from the average actual travel time and the loading and unloading time of vehicles in the system. The alternative of rearranging hangar used is based on the development plan of the Giwangan Market Management Office. The simulation model has been verified and validated. The first rearrangement scenario is that all traders are moved to a new location (slaughterhouse). Second, the flow in and out of the hangar is one-way. Thus, the average vehicle in the system becomes 54.44 minutes and the hangar utility is 16.13% as an indicator that the hangar performance can be improved by increasing the number of vehicles capable of loading and unloading.

A proactive route planning approach to navigation errors

ABSTRACT Online navigation systems assume a person can follow a given route from origin to destination. Nonetheless, spatial cognition studies show that wayfinding is a complex, highly adaptive process and that route planning is incremental rather than prescriptive. Indeed, people may deviate from their originally chosen route for a number of reasons including navigation errors, especially when the environment is unfamiliar. Even in familiar places, certainty in wayfinding is highly unlikely to be completely achieved. Consequently, when people make a wrong turn or miss an exit, even the best reroute to the destination may add several minutes to the originally planned travel time. This work formally defines the novel problem of finding a path such that, when navigation errors occur, recovering is not as costly. We call this approach the most recoverable path. This subtle change in the route planning problem – i.e., considering error recovery costs – invalidates using dynamic programming, such as in shortest path algorithm solutions. We therefore introduce a novel, readily applicable, fast heuristic to this NP-hard problem. The benefits of the most recoverable path are manifold: long detours are avoided, actual travel time is reduced, and it is comparable to its shortest counterpart in terms of length.