Higher Travel Time Research Articles

Energy harvesting for automated storage and retrieval system with sustainable configuration of storage assignment and input/output point

The warehouse automation market has experienced significant growth due to the necessity for quick responses to customer needs. The adoption of Automated Storage and Retrieval System (AS/RS) aims to enhance operational efficiency and expedite order fulfillment, although environmental considerations are frequently overlooked. This study introduces the implementation of energy harvesting using Regenerative Braking System (RBS) on AS/RS to minimize the carbon emission impact. The best configuration of storage assignments and Input/Output (I/O) points is examined to improve travel time, response time, and carbon emission as sustainability indicators. This study employs a discrete-event simulation mimicking the AS/RS and warehouse environment under uncertainty. Simulation-based experiment was performed under 96 different scenarios and the result was assessed through statistical tests revealing the main and interaction effects between factors to performance indicators, including the trade-off between them. The result reveals that the implementation of RBS in AS/RS can result in 13% energy saving on average or equal to additional travel range of 28,800 m indicating the suitability adoption towards green operation. However, the lowest carbon emission is followed by higher travel time and response time. Thus, metamodel-based optimization was also performed via desirability function analysis. The optimization result reveals that the sustainable AS/RS configuration is obtained with a single-side for I/O point, non-class for storage classification, closest open location with column-order for slot selection, and closest open location with row-order for retrieval selection.

Analysis of travel burden and travel support among patients treated at a comprehensive cancer center in the Southeastern United States.

Travel burden leads to worse cancer outcomes. Understanding travel burden and the level and types of travel support provided at large cancer centers is critical for developing systematic programs to alleviate travel burden. This study analyzed patients who received travel assistance, including their travel burden, types and amount of travel support received, and factors that influenced these outcomes. We analyzed 1063 patients who received travel support from 1/1/2021 to 5/1/2023 at Winship Cancer Institute, in which ~18,000 patients received cancer care annually. Travel burden was measured using distance and time to Winship sites from patients' residential address. Travel support was evaluated using the monetary value of total travel support and type of support received. Patients' sociodemographic and clinical factors were extracted from electronic medical records. Area-level socioeconomic disadvantage was coded by the Area Deprivation Index using patient ZIP codes. On average, patients traveled 57.2 miles and 67.3min for care and received $74.1 in total for travel support. Most patients (88.3%) received travel-related funds (e.g., gas cards), 5% received direct rides (e.g., Uber), 3.8% received vouchers for taxi or public transportation, and 3% received combined travel support. Male and White had longer travel distance and higher travel time than female and other races, respectively. Patients residing in more disadvantaged neighborhoods had an increased travel distance and travel time. Other races and Hispanics received more travel support ($) than Black and White patients or non-Hispanics. Patients with higher travel distance and travel time were more like to receive travel-related financial support. Among patients who received travel support, those from socioeconomically disadvantaged neighborhoods had greater travel burden. Patients with greater travel burden were more likely to receive travel funds versus other types of support. Further understanding of the impact of travel burden and travel support on cancer outcomes is needed.

Development of Gresik – Mojokerto Railway Infrastructure to Accelerate Economic Growth in Gerbangkertosusila

The most important economic area in East Java is the Gerbangkertosusila (GKS) area. The GKS area between Gresik – Mojokerto is not yet connected directly, almost all goods have to go through Surabaya and Sidoarjo first. This results in high logistics costs and long travel times. The research method used in this research is a qualitative research method on economic growth factors in the GKS area. The factors considered in economic growth are population, number of people of productive age, number of people who are not yet working, minimum income/month, number of poorer people, percentage of poorer people, length of roads, number of people accessing the internet, number of telecommunication towers, length of roads damaged, investment value, export value, import value, and gross regional domestic product (GRDP). The analytical tool used in the research is a descriptive method to measure and assess the quality of economic growth and changes in regional economic growth factors, assisting strategic planning for the construction of new railway lines as the right solution for developing regional economic growth. Therefore, to accelerate economic growth and reduce logistics costs in the GKS area, a new railway line between Gresik - Mojokerto should be built. If logistics costs are low, it will reduce the price of goods and increase the opportunity to order goods and open up new jobs. If new job opportunities are opened, it will be possible to reduce the number of unemployed people by 375,392 people and the poorer population by 719,944 people. So it is hoped that it will increase economic growth to reach 7%. Only the Gresik and Sidoarjo regions have met this economic growth. Mojokerto’s economic growth is still 5.56% and lags behind other regions. If the construction of the new Gresik – Mojokerto train line is realized, then economic growth in the GKS region of at least 7% will not be difficult to achieve.

The Importance of Implementation of Traffic Light Optimization System: Greece Case Study

In recent years, vehicle traffic has become a major issue, with a significant increase in the number of vehicles leading to congestion on urban roads and motorways. This has resulted in an increase in accidents, causing more injuries and fatalities. In particular the cases with seriously injured road users who may not survive due to traffic congestion delaying emergency services. This paper tries to analyse the importance of the response time of the emergency services during road accidents, using real data from a hybrid environment (motorway and urban road). The presented model in this research aims to identifying the traffic problems, where the traffic lights are found to cause congestion issues and high travel times, even with low traffic volumes. The implementation of a traffic lights optimization system can be a crucial element in survival chances. The study also shows that an increase in traffic volume leads to drastic traffic congestion, highlighting further the importance of the traffic light optimization system.

Travel burden for patients with multimorbidity – Proof of concept study in a Dutch tertiary care center

ObjectivesTo explore travel burden in patients with multimorbidity and analyze patients with high travel burden, to stimulate actions towards adequate access and (remote) care coordination for these patients. DesignA retrospective, cross-sectional, explorative proof of concept study. Setting and ParticipantsElectronic health record data of all patients who visited our academic hospital in 2017 were used. Patients with a valid 4-digit postal code, aged ≥18 years, had >1 chronic or oncological condition and had >1 outpatient visits with >1 specialties were included. MethodsTravel burden (hours/year) was calculated as: travel time in hours × number of outpatient visit days per patient in one year × 2. Baseline variables were analyzed using univariate statistics. Patients were stratified into two groups by the median travel burden. The contribution of travel time (dichotomized) and the number of outpatient clinic visits days (dichotomized) to the travel burden was examined with binary logistic regression by adding these variables consecutively to a crude model with age, sex and number of diagnosis. National maps exploring the geographic variation of multimorbidity and travel burden were built. Furthermore, maps showing the distribution of socioeconomic status (SES) and proportion of older age (≥65 years) of the general population were built. ResultsA total of 14 476 patients were included (54.4% female, mean age 57.3 years ([± standard deviation] = ± 16.6 years). Patients travelled an average of 0.42 (± 0.33) hours to the hospital per (one-way) visit with a median travel burden of 3.19 hours/year (interquartile range (IQR) 1.68 – 6.20). Care consumption variables, such as higher number of diagnosis and treating specialties in the outpatient clinic were more frequent in patients with higher travel burden. High travel time showed a higher Odds Ratio (OR = 578 (95% Confidence Interval (CI) = 353 – 947), p < 0.01) than having high number of outpatient clinic visit days (OR = 237, 95% CI = 144 – 338), p < 0.01) to having a high travel burden in the final regression model. Conclusions and implicationsThe geographic representation of patients with multimorbidity and their travel burden varied but coincided locally with lower SES and older age in the general population. Future studies should aim on identifying patients with high travel burden and low SES, creating opportunity for adequate (remote) care coordination.

A GIS Approach to Measuring Public Transport Travel Delay on Higher Order Roads in the City of Cape Town

Abstract. On a daily basis, hundreds of thousands of people in the City of Cape Town rely on road-based public transport as a means of commuting. A major factor that influences the passengers’ experience is the travel time to reach a desired destination. Due to various physical and operational factors, some sections of the road network experience high congestion and travel time delays during the morning and afternoon peak periods. Quantifying these delays, and the number of road-based public transport passengers exposed to the delay along a specific road section during a peak period allows informed decision-making and prioritisation. GIS and spatial data analysis are powerful tools to determine where and when such delays occur. Various spatial data sets exist that were jointly analysed to quantify the delay and the passenger volumes exposed to the delay on the city’s public transport network.

Evaluating Traffic Operation Conditions during Wildfire Evacuation Using Connected Vehicles Data

With climate change and the resulting rise in temperatures, wildfire risk is increasing all over the world, particularly in the Western United States. Communities in wildland–urban interface (WUI) areas are at the greatest risk of fire. Such fires cause mass evacuations and can result in traffic congestion, endangering the lives of both citizens and first responders. While existing wildfire evacuation research focuses on social science surveys and fire spread modeling, they lack data on traffic operations during such incidents. Additionally, traditional traffic data collection methods are unable to gather large sets of data on historical wildfire events. However, the recent availability of connected vehicle (CV) data containing lane-level precision historical vehicle movement data has enabled researchers to assess traffic operational performance at the region and timeframe of interest. To address this gap, this study utilized a CV dataset to analyze traffic operations during a short-notice evacuation event caused by a wildfire, demonstrating that the CV dataset is an effective tool for accurately assessing traffic delays and overall traffic operation conditions during the selected fire incident. The findings also showed that the selected CV dataset provides high temporal coverage and similar travel time estimates as compared to an alternate method of travel time estimation. The study thus emphasized the importance of utilizing advanced technologies, such as CV data, to develop effective evacuation strategies and improve emergency management.

Approaches to Managing Speed in New Zealand’s Capital

Higher speeds increase the risk of crashes occurring and the severity of resulting injuries. The purpose of speed management is to ensure that speeds are safe for all road users thereby reducing the number of crashes and avoid injuries and deaths when crashes happen. In response to a rising trend in deaths from road crashes in recent years, the New Zealand Government introduced the Road to Zero road safety strategy in 2018 and updated the Land Transport Rule: Setting of Speed Limits in 2022. In Wellington city (New Zealand’s capital), unsafe speed limits and high numbers of road crashes with injuries make city streets unsafe, have high social costs, present a barrier to active transport, and limit mode choice for residents and visitors. This study assessed nine speed management options using cost benefit analysis. Citywide permanent speed reduction was most effective for reducing road crash-related injuries. The option with 30 km/h speed limit for local streets and 40 km/h for arterial streets had the highest crash reduction benefits of over NZD $500 million, discounted over 40 years followed by the option of 30 km/h default citywide, albeit with the high construction costs and relatively high vehicle travel time disbenefits. Incremental cost benefit calculations indicated that, depending on the total budget available, the most efficient options were permanent speed reductions near schools and the 30/40 km/h mix citywide. Implementing variable speed limits around schools provided the lowest benefits and very low value for money compared to citywide speed management approaches or permanently reduced speed around schools.

Traffic impacts of innovative traffic and parking arrangements in Athens, Greece

In the city of Athens, traffic delays and high travel times are noticed due to the high levels of inner-city traffic. Consequently, sustainable mobility practices, such as enlarging sidewalks, parking management and prioritizing pedestrians and public transport are imperatively required. Within the framework of the new Sustainable Urban Mobility Plan of the city of Athens, a series of novel traffic and parking arrangements were examined, promoting public transport and active travel modes. This paper aims to assess the traffic impacts of the arrangements, examine the interventions that upgrade public space in Athens city and present an evaluation of their pilot implementation. The results indicated that, the evaluation of the pilot implementation verified the simulation model forecasts and presented significant changes in citizen habits shifting to more environmentally modes of transport.

City-Level Travel Time and Individual Dietary Consumption in Latin American Cities: Results from the SALURBAL Study.

There is limited empirical evidence on how travel time affects dietary patterns, and even less in Latin American cities (LACs). Using data from 181 LACs, we investigated whether longer travel times at the city level are associated with lower consumption of vegetables and higher consumption of sugar-sweetened beverages and if this association differs by city size. Travel time was measured as the average city-level travel time during peak hours and city-level travel delay time was measured as the average increase in travel time due to congestion on the street network during peak hours. Vegetables and sugar-sweetened beverages consumption were classified according to the frequency of consumption in days/week (5–7: “frequent”, 2–4: “medium”, and ≤1: “rare”). We estimate multilevel ordinal logistic regression modeling for pooled samples and stratified by city size. Higher travel time (Odds Ratio (OR) = 0.65; 95% Confidence Interval (CI) 0.49–0.87) and delay time (OR = 0.57; CI 0.34–0.97) were associated with lower odds of frequent vegetable consumption. For a rare SSB consumption, we observed an inverse association with the delay time (OR = 0.65; CI 0.44–0.97). Analysis stratified by city size show that these associations were significant only in larger cities. Our results suggest that travel time and travel delay can be potential urban determinants of food consumption.

Joint clustering and prediction approach for travel time prediction.

Modeling and prediction of traffic systems is a challenging task due to the complex interactions within the system. Identification of significant regressors and using them to improve travel time predictions is a concept of interest. In previous studies, such regressors were identified offline and were static in nature. In this study, an iterative joint clustering and prediction approach is proposed to accurately predict spatiotemporal patterns in travel time. The clustering module is tied to the prediction module, and a prediction model is trained on each cluster. The combined clustering and prediction are then iterated until a chosen metric is optimized. This orients clusters of data towards prediction while enabling model development on subsets of travel time data with similar prediction complexity. The clusters created using the joint clustering and prediction approach confirmed to the real-world traffic scenario, forming clusters of high travel time at busy intersections and bus stops across the study stretch and forming clusters of low travel time in the sub-urban areas of the city. Further, a comparison of the developed framework with base methods demonstrated a decrease in prediction errors by at least 22.83%. This indicates that creating clusters of data that are sensitive to the quality of predictions using the joint clustering and prediction framework improves the accuracy of travel time predictions. The study also proposes criteria for choosing the best predictions when cluster-based predictions are used.

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.

Evaluating the cognitive and psychological effects of real-time auditory travel information on drivers using EEG

ABSTRACT Real-time travel information design with inadequate consideration of human factors can lead to driver distraction and diminish road safety. This study measures drivers’ brain electrical activity patterns to evaluate multiple aspects of driver cognition and psychology under real-time information provision using insights from the neuroscience domain on the localisation of brain functions. The brain electrical activity patterns of 84 participants are collected using an electroencephalogram (EEG) in an interactive driving simulator environment. The impacts of real-time auditory travel information characteristics (amount, sufficiency, and content) and different time stages of interaction with information provision (before, during, and after) on the frequency band powers of EEG signals in different brain regions are analyzed using linear mixed models. Study results illustrate that drivers exert more cognitive effort to perceive/process information on routes with complex driving environments. Insufficient information may evoke increased attention to internal processing and memory processing on routes characterised by higher travel time uncertainty, while route recommendation to switch to such routes may increase drivers’ stress and anxiety. The study findings can aid information providers, both private and public, as well as auto manufacturers to incorporate driver cognition in designing safer real-time information and their delivery systems.

High Volume Freeway Travel Time Reliability and the COVID-19 Pandemic

This paper examines the effect of the COVID-19 pandemic on travel time reliability (TTR) performance on two high traffic volume freeway corridors (I-90 and I-405) in the U.S.A. Specifically, the travel time distributions (TTDs) during and before the pandemic are compared. The paper also examines which TTR metrics best capture the effect of the pandemic on reliability performance. There were statistically significant differences, at the 95% confidence level, between TTD in 2020 and corresponding TTDs in 2018 and 2019. Not surprisingly, all measures of central tendency and all measures of dispersions were reduced during the pandemic. Consequently, it was concluded that TTR performance improved during the pandemic regardless of what TTR definition was used. Travel time index (TTI), planning time index (PTI), and the level of travel time reliability (LOTTR) metrics improved during the pandemic, albeit at different rates. In contrast, the buffer index and coefficient of variation increased. In other words, whether an analyst would identify that TTR improved or decreased during the pandemic, and by how much, would be a function of which TTR metric was applied. Not surprisingly, the more congested the roadway section, the greater the impact the pandemic-related interventions had on TTR. It was found that, practically speaking, there are no differences in TTI, PTI, or LOTTR values when TTDs are formulated using a 5 min or 15 min aggregation interval. It was concluded that analysts need to have a deep understanding of the underlying TTD and the various TTR definitions when evaluating changes in freeway systems’ TTR.

Dynamic system optimal traffic assignment with atomic users: Convergence and stability

In this study, we analyse the convergence and stability of dynamic system optimal (DSO) traffic assignment with fixed departure times. We first formulate the DSO traffic assignment problem as a strategic game wherein atomic users select routes that minimise their marginal social costs, called a ‘DSO game’. By utilising the fact that the DSO game is a potential game, we prove that a globally optimal state is a stochastically stable state under the logit response dynamics, and the better/best response dynamics converges to a locally optimal state. Furthermore, as an application of DSO assignment, we examine characteristics of the evolutionary implementation scheme of marginal cost pricing. Through theoretical comparison with a fixed pricing scheme, we found the following properties of the evolutionary implementation scheme: (i) the total travel time decreases smoother to an efficient traffic state as congestion externalities are perfectly internalised; (ii) a traffic state would reach a more efficient state as the globally optimal state is stabilised. Numerical experiments also suggest that these properties make the evolutionary scheme robust in the sense that they prevent a traffic state from going to worse traffic states with high total travel times.

Unsupervised deep domain adaptation framework in remote acoustic time parametric imaging

Intelligent waveform analysis surveying geological structures is a challenging task in remote acoustic measurement for formation detection, which has two problems: (1) time parametric imaging is disturbed by noisy environments and (2) manually annotated data for machine learning are unattainable. These restrict the deployment of advanced parameter extraction methods in imaging instruments. As a potential theory, domain adaptation makes the intelligent prediction implementable in the above situations. Hence, to counter high precision travel time extraction for acoustic imaging, a deep adaptation-picking network (DAPN) framework is proposed, which consists of three modules: signal-to-signal generator, domain adaptation encoder, and feature recognition decoder. The translation module preprocesses the different datasets to improve the training accuracy and confuses the distribution between source and target domains based on the adversarial network. The core convolution neural network achieves travel time measurement, where the backbone extracts content features with modified maximum mean discrepancy embedding. Meanwhile, the decoding structure preserves the signal features in the training process. The effectiveness and feasibility of DAPN are verified by experimental results, suggesting that DAPN accurately extracts time parameters. Compared with traditional parametric imaging schemes, the proposed method has the advantages of high accuracy and anti-noise ability.

Proximate Causes of Forest Degradation in the Democratic Republic of the Congo Vary in Space and Time

Forest degradation, generally defined as a reduction in the delivery of forest ecosystem services, can have long-term impacts on biodiversity, climate, and local livelihoods. The quantification of forest degradation, its dynamics and proximate causes can help prompt early action to mitigate carbon emissions and inform relevant land use policies. The Democratic Republic of the Congo is largely forested with a relatively low deforestation rate, but anthropogenic degradation has been increasing in recent years. We assess the impact of eight independent variables related to land cover, land use, infrastructure, armed conflicts, and accessibility on forest degradation, measured by the Forest Condition (FC) index, a measure of forest degradation based on biomass history and fragmentation that ranges from 0 (completely deforested) to 100 (intact). We employ spatial panel models with fixed effects using regular 25 × 25 km units over five 3-year intervals from 2002 to 2016. The regression results suggest that the presence of swamp ecosystems, low access (defined by high travel time), and forest concessions are associated with lower forest degradation, while built up area, fire frequency, armed conflicts result in greater forest degradation. The impact of neighboring units on FC shows that all variables within the 50 km spatial neighborhood have a greater effect on FC than the on-site spatial determinants, indicating the greater influence of drivers beyond the 25 km2unit. In the case of protected areas, we unexpectedly find that protection in neighboring locations leads to higher forest degradation, suggesting a potential leakage effect, while protected areas in the local vicinity have a positive influence on FC. The Mann-Kendall trend statistic of occurrences of fires and conflicts over the time period and until 2020 show that significant increases in conflicts and fires are spatially divergent. Overall, our results highlight how assessing the proximate causes of forest degradation with spatiotemporal analysis can support targeted interventions and policies to reduce forest degradation but spillover effects of proximal drivers in neighboring areas need to be considered.

Understanding vehicles commuting pattern based on license plate recognition data

Commuting vehicles are one of the most important vehicles on the road network, especially during morning and evening peak hours. Analysis of commuting vehicles can lay foundation for policy formulation, urban planning, business decision-making. A large number of license plate recognition (LPR) detectors installed on the road network have accumulated a large amount of LPR data that contain the spatio-temporal information on vehicles. Therefore, using the LPR data, the commuting behavior of vehicles can be mined and analyzed. This study takes the LPR data of Hangzhou, China as an example, a series of algorithms are used to extract a total of nine features reflecting the commuting travel behavior using the one-month LPR data, and then the factor analysis method is used to integrate the nine extracted features into three factors. The commuting behavior is characterized from three perspectives: the stability of the vehicle travel behavior during the high frequency travel time period on working days, the stability of the vehicle travel behavior during the non high frequency travel time period, the stability of the origin and destination (OD) of a vehicle during the high frequency travel time period. Based on these three factors, three methods, including the density-based spatial clustering of applications with noise algorithm (DBSCAN), the iterative self-organizing data analysis technique algorithm (ISODATA), and the fast search and find of density peaks clustering algorithm, are used to identify commuting pattern vehicles and their results are compared. Finally, the decision tree algorithm is used to extract the commuting rule and identify commuting vehicles from the collected data. After that, The influence of different high frequency travel time period on commuting rule extraction and commuting pattern vehicle recognition is carefully analyzed. The influence of randomness on the results is also tested. The proposed commuting pattern vehicle recognition algorithm is proved to be robust. The detection frequency and the first and last detected times of commuting vehicles and non commuting vehicles are then analyzed. 78.0% of commuting vehicles were first detected between 06:30 AM and 10:30 AM on each workday on average. And 77.8% of commuting vehicles were last detected between 03:30 PM and 09:30 PM on each workday on average. However, for non commuting vehicles, the phenomenon that start or end the travel during the high frequency travel time period is not obvious. This study lays a foundation for travel policy formulation. For instance, it can provide a reference for the determination of the restricted periods when implementing the travel restriction policy. In addition, the identification of commuting vehicles is extremely useful for public transit network design and optimization, and it can help the decision-making process of a customized commuter bus. In addition, these strategies are expected to reduce the use of private cars and encourage travelers to switch to public transportation.

Mechanisms for overpressure generation in the bonan sag of Zhanhua depression, Bohai Bay Basin, China

The Bonan sag in the Zhanhua Depression is a prolific oil-producing Cenozoic basin located in the Bohai Bay Basin of China. Three overpressure systems have been identified in the Paleogene Dongying Formation to the first interval of the Paleogene Shahejie Formation, the third interval, and fourth interval of the Paleogene Shahejie Formation, respectively. A combination of drill stem test data, well-log responses, geochemical data of source rocks, basin modeling, and vertical effective stress-density and density-sonic velocity cross plots, were used to investigate the distribution and mechanisms of different overpressure systems in the Bonan sag. The sonic log responds to overpressure development with anomalously high acoustic travel time in the Bonan sag. The Eaton method combined with sonic log data was applied to accurately predict the overpressure magnitude of the three overpressure systems. The overpressure mechanisms differ for each overpressure system. The overpressure in the Dongying Formation to the first interval of the Shahejie Formation, with a top depth from 2000 to 3000 m, is attributed to disequilibrium compaction. The combined mechanisms of disequilibrium compaction and oil generation lead to overpressure in the third interval of the Shahejie Formation with a top depth from 2200 to 3700 m. The overpressure in the fourth interval of the Shahejie Formation, with a top depth from 2500 to 4300 m, is mainly caused by disequilibrium compaction, supplemented by the minor influence of oil generation. Mudstones are the sealing rocks for the overpressure systems in the Dongying Formation to the first interval of the Shahejie Formation and the third interval of the Shahejie Formation. Sealing from the overlying gypsum-rich layer is crucial for overpressure preservation in the fourth interval of the Shahejie Formation. The difference in the oil generation capacity of source rocks and sealing condition controls the variation of overpressure mechanisms in the three overpressure systems in the Bonan sag.

Exploring country-wide equitable government health care facility access in Uganda

BackgroundRural access to health care remains a challenge in Sub-Saharan Africa due to urban bias, social determinants of health, and transportation-related barriers. Health systems in Sub-Saharan Africa often lack equity, leaving disproportionately less health center access for the poorest residents with the highest health care needs. Lack of health care equity in Sub-Saharan Africa has become of increasing concern as countries enter a period of simultaneous high infectious and non-communicable disease burdens, the second of which requires a robust primary care network due to a long continuum of care. Bicycle ownership has been proposed and promoted as one tool to reduce travel-related barriers to health-services among the poor.MethodsAn accessibility analysis was conducted to identify the proportion of Ugandans within one-hour travel time to government health centers using walking, bicycling, and driving scenarios. Statistically significant clusters of high and low travel time to health centers were calculated using spatial statistics. Random Forest analysis was used to explore the relationship between poverty, population density, health center access in minutes, and time saved in travel to health centers using a bicycle instead of walking. Linear Mixed-Effects Models were then used to validate the performance of the random forest models.ResultsThe percentage of Ugandans within a one-hour walking distance of the nearest health center II is 71.73%, increasing to 90.57% through bicycles. Bicycles increased one-hour access to the nearest health center III from 53.05 to 80.57%, increasing access to the tiered integrated national laboratory system by 27.52 percentage points. Significant clusters of low health center access were associated with areas of high poverty and urbanicity. A strong direct relationship between travel time to health center and poverty exists at all health center levels. Strong disparities between urban and rural populations exist, with rural poor residents facing disproportionately long travel time to health center compared to wealthier urban residents.ConclusionsThe results of this study highlight how the most vulnerable Ugandans, who are the least likely to afford transportation, experience the highest prohibitive travel distances to health centers. Bicycles appear to be a “pro-poor” tool to increase health access equity.