Changes In Travel Time Research Articles

Effectiveness of travel behavior and infrastructure change to mitigate heat exposure

Urban heat exposure is an increasing health risk among urban dwellers. Many cities are considering accommodating active mobility, especially walking and biking, to reduce greenhouse gas emissions. However, promoting active mobility without proper planning and transportation infrastructure to combat extreme heat exposure may cause more heat-related morbidity and mortality, particularly in future with projected climate change. This study estimated the effectiveness of active trip heat exposure mitigation under built environment and travel behavior change. Simulations of the Phoenix metro region's 624,987 active trips were conducted using the activity-based travel model (ABM), mean radiant temperature (TMRT, net human radiation exposure), transportation network, and local climate zones. Two scenarios were designed to reduce traveler exposure: one that focuses on built environment change (making neighborhoods cooler) and the other on travel behavior (switching from shorter travel time but higher exposure routes to longer travel time but cooler routes) change. Travelers experienced TMRT heat exposure ranging from 29°C to 76°C (84°F to 168°F) without environmental or behavioral change. Active trip TMRT exposures were reduced by an average of 1.2–3.7°C when the built environment was changed from a hotter to cooler design. Behavioral changes cooled up to 10 times more trips than changes in built environment changes. The marginal benefit of cooling decreased as the number of cooled corridors transformed increased. When the most traveled 10 km of corridors were cooled, the marginal benefit affected over 1,000 trips/km. However, cooling all corridors results in marginal benefits as low as 1 trip/km. The results reveal that heavily traveled corridors should be prioritized with limited resources, and the best cooling results come from environment and travel behavior change together. The results show how to surgically invest in travel behavior and built environment change to most effectively protect active travelers.

Seasonal Variations of Seismic Travel-Time Changes in Binchuan, Southwestern China, Inferred from Large Volume Air-Gun Source Array Data

AbstractActive sources, especially air-gun sources in the water reservoir, have proven to be powerful tools for detecting regional scale velocity changes. However, the water level change affects the repeatability of the air-gun waveform and, thus, affects the stability of the detection of the velocity changes. This article explores how to make full use of the air-gun signals excited at different water levels from analyzing three years of air-gun data recorded by 20 stations deployed from ∼50 m to ∼25 km from the source. At the same time, by utilizing the poroelastic model, we quantify both vertical and horizontal distances affected by the water level change. More important, supported by the strain data from one borehole strainmeter station, the influence mechanisms of the seasonal variation derived from the three years of air-gun data are also discussed. Results indicate the water level affects the main frequency of the air gun and has a strong influence on the coda wave. When the water level of the reservoir changes abruptly, the dominant effect on the derived delay time change is from the water level change. In this case, the deconvolution method can hardly eliminate the influence of the abrupt water level change. However, when the reservoir's water level changes gently, the delay time varies accordingly rather than inversely with the water level. Other reasons affect the material properties and, thus, influence the derived delay time. The modeled vertical component of poroelastic strain caused by the reservoir water level change is 1×10−7. The observed strain (4×10−7) from the strainmeter is likely associated with thermoelastic strain induced by temperature change. Our results show that although the long-term air-gun signal is affected by water level, there is still much information about changes in the subsurface that is worth mining.

Analyzing indirect economic impacts of wildfire damages on regional economies.

This article estimates the economic impacts of wildfire damage on Korea's regional economies, developing an integrated disaster-economic system for Korea. The system is composed of four modules: an interregional computable general equilibrium (ICGE) model for the eastern mountain area (EMA) and the rest of Korea, a Bayesian wildfire model, a transportation demand model, and a tourist expenditure model. The model has a hierarchical structure, with the ICGE model serving as a core module to link to three other modules. In the impact analysis of a wildfire, three external shocks are injected into the ICGE model: (1) the wildfire damaged area derived from the Bayesian wildfire model, (2) changes in travel times among cities and counties derived from the transportation demand model, and (3) variations in visitors' expenditures derived from the tourist expenditure model. The simulation shows that the gross regional product (GRP) of the EMA would decrease by 0.25% to 0.55% without climate change and by 0.51%-1.23% with climate change. This article contributes to the development of quantitative linkages between macro and micro spatial models in a bottom-up system for the impact analysis of disasters, integrating a regional economic model with a place-based disaster model and the demands of tourism and transportation.

Extracting small time-lapse traveltime changes in a reservoir using primaries and internal multiples after Marchenko-based target zone isolation

Geophysical monitoring of subsurface reservoirs relies on detecting small changes in the seismic response between a baseline and monitor study. However, internal multiples, related to the over- and underburden, can obstruct the view of the target response, hence complicating the time-lapse analysis. To retrieve a response that is free from the over- and underburden effects, the data-driven Marchenko method is used. This method effectively isolates the target response, which can then be used to extract more precise time-lapse changes. In addition, the method also reveals target-related multiples that probe the reservoir more than once, which further defines the changes in the reservoir. To verify the effectiveness of the method, a numerical example is constructed. This test finds that, when using the isolated target response, the observed time differences resemble the expected time differences in the reservoir. Moreover, the results obtained with target-related multiples also benefit from the Marchenko-based isolation of the reservoir. It is, therefore, concluded that this method has the potential to observe dynamic changes in the subsurface with increased accuracy.

Estimating road traffic impacts of commute mode shifts.

This work considers the sensitivity of commute travel times in US metro areas due to potential changes in commute patterns, for example caused by events such as pandemics. Permanent shifts away from transit and carpooling can add vehicles to congested road networks, increasing travel times. Growth in the number of workers who avoid commuting and work from home instead can offset travel time increases. To estimate these potential impacts, 6-9 years of American Community Survey commute data for 118 metropolitan statistical areas are investigated. For 74 of the metro areas, the average commute travel time is shown to be explainable using only the number of passenger vehicles used for commuting. A universal Bureau of Public Roads model characterizes the sensitivity of each metro area with respect to additional vehicles. The resulting models are then used to determine the change in average travel time for each metro area in scenarios when 25% or 50% of transit and carpool users switch to single occupancy vehicles. Under a 25% mode shift, areas such as San Francisco and New York that are already congested and have high transit ridership may experience round trip travel time increases of 12 minutes (New York) to 20 minutes (San Francisco), costing individual commuters $1065 and $1601 annually in lost time. The travel time increases and corresponding costs can be avoided with an increase in working from home. The main contribution of this work is to provide a model to quantify the potential increase in commute travel times under various behavior changes, that can aid policy making for more efficient commuting.

PTA-Sync: Packet-Train-Aided Time Synchronization for Underwater Acoustic Applications

In underwater acoustic applications, time synchronization errors accumulate severely with time, owing to the unpredictable propagation delay change induced by mobility and low sound propagation speed. In previously reported schemes, error accumulation during underwater acoustic time synchronization occurred, decreasing their performance and affecting their applicability. To overcome this limitation, we propose packet-train-aided time synchronization (PTA-Sync) for underwater acoustic application. The proposed PTA-Sync adaptively tracks one-way travel time (OWTT) change using three-dimensional linear velocity vector, the transmit time difference and arrival time difference of two adjacent packets in a packet train, and the position information of a mobile reference node. Thus, this scheme enables us to reduce the synchronization error accumulation rate by estimating the propagation delay change more accurately. Simulation results show PTA-Sync achieves higher accuracy than existing OWTT-based synchronization schemes. Thus, PTA-Sync can be effectively used in underwater exploratory activities because it can successfully reduce time synchronization errors.

Travel behavior on vacation: transport mode choice of tourists at destinations

• A study on tourist mobility that employs revealed preference travel-activity data. • Precise computation of mode attributes (time, cost) extends existing approaches. • Company, trip purpose, destination knowledge and weather also influence mode choice. • Tourists are inelastic to changes in price, but react to changes in travel time. • Accounting for tourist on-site travel is essential for accurate policy design. Tourist travel contributes greatly to transport problems in highly attractive tourist areas. Despite that, local travel behavior of visitors at the destination has hardly been investigated so far. To fill this gap, we used data from a bespoke travel-activity survey conducted in the Austrian Alps to develop models of transport mode choice of tourists during their vacation stays at the resorts. The outcomes reveal significant effects of, inter alia, travel time, travel cost, travel party composition, trip purpose, respondent’s level of fitness, their knowledge about long-distance travel to the destination and mobility options at the destination, and selected weather elements. Tourists are found to be very inelastic to changes in travel cost, whilst more responsive to changes in travel time. The paper delivers unique evidence that can advance transport policy design and thus contribute to more sustainable travel at tourist destinations.

Estimated Travel Time and Spatial Access to Abortion Facilities in the US Before and After the Dobbs v Jackson Women’s Health Decision

Abortion facility closures resulted in a substantial decrease in access to abortion care in the US. To investigate the changes in travel time to the nearest abortion facility after the Dobbs v Jackson Women's Health Organization (referred to hereafter as Dobbs) US Supreme Court decision. Repeated cross-sectional spatial analysis of travel time from each census tract in the contiguous US (n = 82 993) to the nearest abortion facility (n = 1134) listed in the Advancing New Standards in Reproductive Health database. Census tract boundaries and demographics were defined by the 2020 American Community Survey. The spatial analysis compared access during the pre-Dobbs period (January-December 2021) with the post-Dobbs period (September 2022) for the estimated 63 718 431 females aged 15 to 44 years (reproductive age for this analysis) in the US (excluding Alaska and Hawaii). The Dobbs ruling and subsequent state laws restricting abortion procedures. The pre-Dobbs period measured abortion access to all facilities providing abortions in 2021. Post-Dobbs abortion access was measured by simulating the closure of all facilities in the 15 states with existing total or 6-week abortion bans in effect as of September 30, 2022. Median and mean changes in surface travel time (eg, car, public transportation) to an abortion facility in the post-Dobbs period compared with the pre-Dobbs period and the total percentage of females of reproductive age living more than 60 minutes from abortion facilities during the pre- and post-Dobbs periods. Of 1134 abortion facilities in the US (at least 1 in every state; 8 in Alaska and Hawaii excluded), 749 were considered active during the pre-Dobbs period and 671 were considered active during a simulated post-Dobbs period. Median (IQR) and mean (SD) travel times to pre-Dobbs abortion facilities were estimated to be 10.9 (4.3-32.4) and 27.8 (42.0) minutes. Travel time to abortion facilities in the post-Dobbs period significantly increased (paired sample t test P <.001) to an estimated median (IQR) of 17.0 (4.9-124.5) minutes and a mean (SD) of and 100.4 (161.5) minutes. In the post-Dobbs period, an estimated 33.3% (sensitivity interval, 32.3%-34.8%) of females of reproductive age lived in a census tract more than 60 minutes from an abortion facility compared with 14.6.% (sensitivity interval, 13.0%-16.9%) of females of reproductive age in the pre-Dobbs period. In this repeated cross-sectional spatial analysis, estimated travel time to abortion facilities in the US was significantly greater in the post-Dobbs period after accounting for the closure of abortion facilities in states with total or 6-week abortion bans compared with the pre-Dobbs period, during which all facilities providing abortions in 2021 were considered active.

Seismic velocity changes in the Groningen reservoir associated with distant drilling

In this study, we show that passively recorded data of nearby passing trains by a deep borehole geophone array could be linked to fluctuations of the gas-water contact in the Groningen reservoir in The Netherlands. During a period of 1.5 months, changes of inter-geophone P-wave travel times were detected by deconvolution interferometry of the recorded train signals. P-to-S converted waves, obtained by deconvolution of the horizontal component by the vertical component at individual geophones, showed simultaneous variations. The observed travel-time changes could be related to fluctuations of the gas-water contact in the observation well caused by pressure variations at a well drilling 4.5 km away. The sim 3.5 day delay between drilling in the reservoir and the seismic response yields a hydraulic diffusivity of approximately 5 m^2/s and suggests that the pressure front is effectively propagated over such a long distance. Our observations illustrate that downhole geophone arrays can be used to monitor changes in the subsurface if repeating noise sources are available, and that unexpected effects may occur due to drilling.

Elasticity of Travel Time and Travel Cost of Private Vehicles and Public Transportation in Bandung, Indonesia

Understanding mechanism of users in responding determinant variables to use mode of transport is beneficial to anticipate the effect of policy. This study aims to identify the elasticity value of travel time and travel cost of the users of private vehicles and public transportation. Using data from users of private vehicles and public transport in Bandung, Indonesia, the elasticity of travel time and travel cost was calculated based on estimated multinomial logit model (MNL) logit. The elasticity was calculated based on gasoline prices, parking fees, transfer fees, access times, travel times, and waiting times. Based on the results of the elasticity values for travel attribute, it was found that in access time and travel time, Trans Metro Bandung (TMB) Bus had the largest elasticity of -0,564 and -5,001, respectively, so TMB Bus was the most sensitive to changes in access time and travel time. In terms of waiting times and fixed costs/gasoline, conventional taxi has the highest elasticity values of -2,630 and -1,604, respectively, so conventional taxi is the most sensitive to changes in waiting time and fixed costs/gasoline.

The Impact of Rail Transit on Accessibility and Spatial Equity of Public Transit: A Case Study of Guangzhou, China.

The urban rail transit network provides the possibility for people to shift from car to public transit for travel. This paper clarified the relationships among public transit, accessibility, and equity and studied the impact of rail transit on public transit accessibility that incorporates the measure of travel time and transit fare and the impacts’ spatial equity. The results show that rail transit contributes to the similar distribution between high rate of changes of time-based accessibility communities and fare-based accessibility communities, which are located nearby the rail transit lines. The degree of inequity in travel time is higher than the degree in transit fare in two scenarios. Due to the well-connected bus transit in the city center, absolute changes in travel time are slight, while relative changes are high. The rail transit has promoted the improvement of public transit equity in some areas. The difference between the time-based accessibility of Conghua District, northern and southern Baiyun District, Huadu District, Nansha District and southern Panyu District, and other communities is getting smaller, which is conducive to the improvement of spatial equity. The results provide theoretical support for the development of an integrated multimodal public transit system.

Selected Topics of the Past Thirty Years in Ocean Acoustics

This paper reviews some of the highlights of selected topics in ocean acoustics during the thirty years that have passed since the founding of the Journal of Theoretical and Computational Acoustics. Advances in computational methods and computers helped to make computational ocean acoustics a vibrant area of research during that period. The parabolic equation method provides an unrivaled combination of accuracy and efficiency for propagation problems in which the bathymetry, sound speed, and other environmental parameters vary in the horizontal directions. The extension of this approach to cases involving layers that support shear waves has been an active area of research throughout the thirty year period. Interest in basin-scale and global-scale propagation was stimulated by the Heard Island Feasibility Test for monitoring climate change in terms of changes in travel time that occur as the temperature of the ocean rises. Diminishing ice cover in the Arctic, which is one of the consequences of climate change, has stimulated renewed interest in Arctic acoustics during the past decade. Reverberation is a challenging problem that was the topic of a major research program during the beginning of the thirty year period. An innovative approach for making it feasible to solve such problems was applied to data for reverberation from the seafloor and from schools of fish, and some of the findings were featured in Science and Nature. Source localization is one of the core problems in ocean acoustics. When applied on a 2-D array of receivers, an approach based on the eigenvectors of the covariance matrix is capable of separating the signals from different sources from each other, determining when this partitioning step is successful, and tracking sources that cross each other in bearing; one of the advantages of this approach is that it does not require environmental information or solutions of the wave equation. Geoacoustic inversion for estimating the layer structure, wave speeds, density, and other parameters of ocean bottoms has also been a topic of interest throughout the thirty year period.

Methodology for Mapping the Residual Stress Field in Serviced Rails Using LCR Waves.

Non-destructive stress measurement by ultrasonic testing is based on calculating the acoustoelastic modulus obtained from the relationship between material stress and sound wave velocity. A critically refracted longitudinal (LCR) wave, which is a bulk longitudinal wave penetrating below and parallel to the surface below an effective depth, is most suitable for ultrasonic stress measurement tests because it exhibits a relatively large change in travel time in response to a change in stress. In particular, the residual stress distribution through the thickness of the subject can be calculated if transducers of different frequencies are applied because of the characteristic of propagation to different depths of penetration depending on the frequency. The main purpose of this study was to visualize the internal or residual stress distribution through the thickness of rails using LCR waves. To this end, LCR probes with different center frequencies were designed and manufactured, and the residual stress values ​​of an unused railroad rail and two used railroad rails operated under different conditions were calculated. This was done using the ultrasonic signals received from each probe, of which the distributions were mapped. Through these mapping results, different residual stress values could be calculated according to the depth. The differences in residual stress generation and distribution according to the conditions surrounding the contact between train wheels and rails, and their characteristics, were visualized and analyzed. As a result, it could be concluded that the non-destructive evaluation technique using LCR waves could detect differences in the residual stress of a rail, and thus can be used to measure the residual stress of the rail accurately.

Multi-Shift Single-Vehicle Routing Problem Under Fuzzy Uncertainty During the COVID-19 Pandemic

Background: This work studies the single vehicle routing problem (VRP) with multishift and fuzzy uncertainty. In this case, a company perpetually exploits a vehicle to accomplish demand over a scheduling period of several work shifts. In our problem, a crew performs maintenance jobs at different locations. The working team operates in different shifts with a maximum duration but recurrently returns to the depot by the end of the shift to avoid overtime. Methods: The objective is to minimize the number of shifts and the completion time (makespan). In addition, we analyze the influence of uncertainty in driving and processing times on the overtime avoidance constraint in shift duration. We develop an Artificial Immune Heuristic to determine optimal solutions considering both makespan and overtime avoidance. We implement a Pareto-based framework to evaluate the impact of uncertainty. Results: We present several numerical case studies to examine the problem. In particular, we analyze different case study scenarios inferred from the environmental changes in travel and processing times observed in the Apulia region (SE Italy) during the COVID-19 lockdown periods that occurred in spring (started on March 9, 2020) and autumn (after November 6, 2020) of the year 2020. Conclusion: The work program was revised as soon as the Italian COVID-19 restrictions were implemented in the spring and autumn of 2020 due to the changing environment. Our approach allowed for the rapid release of new robust maintenance programs. Results show significant improvements with the presented approach.

Attended home delivery under uncertain travel and response time: a case of Indian public distribution system

PurposeBeing the final end of the logistic distribution, attended home delivery (AHD) plays an important role in the distribution network. AHD typically refers to the service provided by the distribution service provider to the recipient's doorstep. Researchers have always identified AHD as a bottleneck for last-mile delivery. This paper addresses a real-life stochastic multi-objective AHD problem in the context of the Indian public distribution system (PDS).Design/methodology/approachTwo multi-objective models are proposed. Initially, the problem is formulated in a deterministic environment, and later on, it is extended to a multi-objective AHD model with stochastic travel and response time. This stochastic AHD model is used to extensively analyze the impact of stochastic travel time and customer response time on the total expected cost and time-window violation. Due to the NP-hard nature of the problem, an ant colony optimization (ACO) algorithm, tuned via response surface methodology (RSM), is proposed to solve the problem.FindingsExperimental results show that a change in travel time and response time does not significantly alter the service level of an AHD problem. However, it is strongly correlated with the planning horizon and an increase in the planning horizon reduces the time-window violation drastically. It is also observed that a relatively longer planning horizon has a lower expected cost per delivery associated.Research limitations/implicationsThe paper does not consider the uncertainty of supply from the warehouse. Also, stochastic delivery failure probabilities and randomness in customer behavior have not been taken into consideration in this study.Practical implicationsIn this paper, the role of uncertainty in an AHD problem is extensively studied through a case of the Indian PDS. The paper analyzes the role of uncertain travel time and response time over different planning horizons in an AHD system. Further, the impact of the delivery planning horizon, travel time and response time on the overall cost and service level of an AHD system is also investigated.Social implicationsThis paper investigates a unique and practical AHD problem in the context of Indian PDS. In the present context of AHD, this study is highly relevant for real-world applications and can help build a more efficient delivery system. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.Originality/valueThe most challenging part of an AHD problem is the requirement of the presence of customers during the time of delivery, due to which the probability of failed delivery drastically increases if the delivery deviates from the customer's preferred time slot. The paper modelled an AHD system to incorporate uncertainties to attain higher overall performance and explore the role of uncertainty in travel and response time with respect to the planning horizon in an AHD, which has not been considered by any other literature.

Health service planning to assess the expected impact of centralising specialist cancer services on travel times, equity, and outcomes: a national population-based modelling study

Centralisation of specialist cancer services is occurring in many countries, often without evaluating the potential impact before implementation. We developed a health service planning model that can estimate the expected impacts of different centralisation scenarios on travel time, equity in access to services, patient outcomes, and hospital workload, using rectal cancer surgery as an example. For this population-based modelling study, we used routinely collected individual patient-level data from the National Cancer Registration and Analysis Service (NCRAS) and linked to the NHS Hospital Episode Statistics (HES) database for 11 888 patients who had been diagnosed with rectal cancer between April 1, 2016, and Dec 31, 2018, and who subsequently underwent a major rectal cancer resection in 163 National Health Service (NHS) hospitals providing rectal cancer surgery in England. Five centralisation scenarios were considered: closure of lower-volume centres (scenario A); closure of non-comprehensive cancer centres (scenario B); closure of centres with a net loss of patients to other centres (scenario C); closure of centres meeting all three criteria in scenarios A, B, and C (scenario D); and closure of centres with high readmission rates (scenario E). We used conditional logistic regression to predict probabilities of affected patients moving to each of the remaining centres and the expected changes in travel time, multilevel logistic regression to predict 30-day emergency readmission rates, and linear regression to analyse associations between the expected extra travel time for patients whose centre is closed and five patient characteristics, including age, sex, socioeconomic deprivation, comorbidity, and rurality of the patients' residential areas (rural, urban [non-London], or London). We also quantified additional workload, defined as the number of extra patients reallocated to remaining centres. Of the 11 888 patients, 4130 (34·7%) were women, 5249 (44·2%) were aged 70 years and older, and 5005 (42·1%) had at least one comorbidity. Scenario A resulted in closures of 43 (26%) of the 163 rectal cancer surgery centres, affecting 1599 (13·5%) patients; scenario B resulted in closures of 112 (69%) centres, affecting 7029 (59·1%) patients; scenario C resulted in closures of 56 (34%) centres, affecting 3142 (26·4%) patients; scenario D resulted in closures of 24 (15%) centres, affecting 874 (7·4%) patients; and scenario E resulted in closures of 16 (10%) centres, affecting 1000 (8·4%) patients. For each scenario, there was at least a two-times increase in predicted travel time for re-allocated patients with a mean increase in travel time of 23 min; however, the extra travel time did not disproportionately affect vulnerable patient groups. All scenarios resulted in significant reductions in 30-day readmission rates (range 4-48%). Three hospitals in scenario A, 41 hospitals in in scenario B, 13 hospitals in scenario C, no hospitals in scenario D, and two hospitals in scenario E had to manage at least 20 extra patients annually. This health service planning model can be used to to guide complex decisions about the closure of centres and inform mitigation strategies. The approach could be applied across different country or regional health-care systems for patients with cancer and other complex health conditons. National Institute for Health Research.

A High-Precision Elastic Reverse-Time Migration for Complex Geologic Structure Imaging in Applied Geophysics

High-precision elastic reverse-time migration (ERTM) imaging has always been one of the trends in the development of geophysics. However, current wavefield simulations using time-domain finite-difference (FD) approaches in ERTM have second-order temporal accuracy, resulting in travel time changes and waveform distortion in wavefield propagation with large time steps, i.e., temporal dispersion. Errors caused by the temporal dispersion can lead to erroneous imaging locations and out-of-focus diffraction events. A new ERTM and its workflow are established here using temporal and spatial high-order FD accuracy wavefields and the vector-based imaging condition. Our method computes elastic vector-based wavefields by solving a P- and S-wave decomposition form of a quasi-stress–velocity equation. An advanced finite-difference scheme is employed in the wavefield solution to achieve simulation with temporal fourth-order accuracy and spatial arbitrary even-order accuracy. The normalized dot-product imaging condition of the source and receiver P/S wavefields is then applied to generate high-quality images. The elastic wavefield simulation and ERTM numerical examples presented here reveal that the anti-dispersion workflow can improve modeling and imaging accuracy. In addition, the field data application shows that our method can achieve reasonable and reliable ERTM images. This method can integrate the most advanced imaging techniques into this computational framework to improve imaging accuracy.

Pharmacy deserts: More than where pharmacies are

BackgroundIn the United States, geographic access is a major driver of health care disparities. Studies have shown that pharmacy deserts are prevalent in the United States, even in major metropolitan areas. However, one limitation often cited by these studies is the use of distance rather than travel time to define pharmacy deserts. ObjectiveThe aim of this study was to assess pharmacy deserts using travel time and to provide a more holistic approach by incorporating analysis of private vehicles and public transportation. MethodsPharmacy details were collected from the National Provider Identifier database and neighborhood characteristics from collected census data for the four largest U.S. cities. Pharmacy access was evaluated using open-source routing engines. We determined neighborhoods in pharmacy deserts using both distance and travel time analyses. Sensitivity analysis was performed to determine changes to pharmacy deserts based on small changes in travel time. ResultsOf 4654 neighborhoods identified in the four cities of interest, 670 (14.4%) neighborhoods were in pharmacy deserts based on distance. Despite accounting for 28.9% of all neighborhoods, predominantly white neighborhoods only accounted for 4.3% of pharmacy deserts. When evaluating pharmacy deserts by car and public transportation, predominantly white neighborhoods accounted for 2.3% and 1.7% of total pharmacy deserts, respectively. Finally, by reducing travel time from 15 minutes to 10 minutes, pharmacy deserts by car and public transportation increased by 105% and 199%, respectively. All but 3 of the new pharmacy deserts found in the sensitivity analysis were found in nonpredominantly white neighborhoods. ConclusionUsing travel time and incorporating modes of transportation, we found that disparities in pharmacy access are more than just where pharmacies are located geographically. There are additional layers of disparities, such as access to public transportation, that need to be addressed to reduce the number of pharmacy deserts.

Analysis of Public Transportation Mode Choices in Tangerang City, Indonesia

Mode choice of transportation is a transportation planning process that functions in determining travel expenses or can be interpreted as an effort to separate people’s travel to understand the relationship between modes and a factor that influences the choice of other modes. This study aims to determine the mode choice of public transportation and the probability of using the Tayo Bus and City Transportation in Tangerang City. The research approach used is quantitative with primary data sources obtained from closed questionnaire instruments with stated preference techniques for 50 respondents using the Tayo Bus on the Ciledug-TangCity route and 50 respondents using the City Transportation for the Ciledug- Cikokol route. The analysis of mode choice uses the binomial logit model with attribute variables in the form of changes in travel costs, changes in travel time, and changes in headway. The results of the analysis show that the probability of mode choice for the changes in these attributes is 92.107% or Tayo Bus and 7.893% for City Transportation. The main factor that underlies the opportunity to choose the Tayo Bus is the cheaper cost than City Transportation, while the opportunity for choosing City Transportation the ease of access felt by passengers. Changes in the attributes of travel costs in both modes are increased or decreased, passengers generally prefer to use the Tayo Bus. Likewise with the attributes of travel time and headway, although it is increased or decreased, passengers tend to prefer to use the Tayo Bus as a mode to get their destination Keywords: mode choice, stated preference, and binomial logit model.

Bi Objective Peer-to-Peer Ridesharing Model for Balancing Passengers Time and Costs

Ride-sharing services are one of the top growing sustainable transportation trends led by mobility-as-a-service companies. Ridesharing is a system that provides the ability to share vehicles on similar routes for passengers with similar or nearby destinations on short notice, leading to decreased costs for travelers. At the same time, though, it takes longer to get from place to place, increasing travel time. Therefore, a fundamental challenge for mobility service providers should be finding a balance between cost and travel time. This paper develops an integer bi-objective optimization model that integrates vehicle assignment, vehicle routing, and passenger assignment to find a non-dominated solution based on cost and time. The model allows a vehicle to be used multiple times by different passengers. The first objective seeks to minimize the total cost, including the fixed cost, defined as the supply cost per vehicle, and the operating cost, which is a function of the distance traveled. The second objective is to minimize the time it takes passengers to reach their destination. This is measured by how long it takes each vehicle to reach the passenger’s point of origin and how long it takes to get to the destination. The proposed model is solved using the AUGMECON method and the NSGA II algorithm. A real case study from Sioux Falls is presented to validate the applicability of the proposed model. This study shows that ridesharing helps passengers save money using mobility services without significant change in travel time.