Minimum Travel Time Research Articles

Should Autonomous Vehicles Collaborate in a Complex Urban Environment or Not?

A specialized version of collaborative driving is convoy driving. It is referred to as the practice of driving more than one vehicle consecutively in the same lane with a small inter-vehicle distance, maintaining the same speed. Extensive research has been conducted on convoys of heavy-duty trucks on the highway; however, limited research has studied convoy driving in an urban environment. The complex dynamics of an urban environment require short-lived collaboration with varying numbers of vehicles rather than collaborating over hours. The motivation of this research is to investigate how convoy driving can be realized to address the challenges of an urban environment and achieve the benefits of autonomous driving such as reduced fuel consumption, travel time, improved safety, and ride comfort. In this work, the best-fitted coalitional game framework is utilized to formulate the convoy driving problem as a coalition formation game in an urban environment. A hypothesis is formulated that traveling in a coalition is more beneficial for a vehicle than traveling alone. In connection with this, a coalitional game and an all-comprehensive utility function are designed, modeled, and implemented to facilitate the formation of autonomous vehicle coalitions for convoy driving. Multiple solution concepts, such as the Shapley allocation, the Nucleolus, and the Core, are implemented to solve and analyze the proposed convoy driving game. Furthermore, several coalition formation strategies such as traveling mode selection, selecting optimal coalitions, and making decisions about coalition merging are developed to analyze the behavior of the vehicles. In addition to this, extensive numerical experiments with different settings are conducted to evaluate and validate the performance of the proposed study. The experimental results proved the hypothesis that traveling in a convoy is significantly more beneficial than traveling alone. We conclude that traveling in a convoy is beneficial for coalition sizes of two to four vehicles with an inter-vehicle spacing of less than 4 m considering the limitations of an urban environment. Traveling in a coalition allows vehicles to save on fuel, minimize travel time and enhance safety and comfort. Furthermore, the findings of this research state that achieving the enormous benefits of traveling in a coalition requires finding the right balance between inter-vehicle distance and coalition size. In the future, we plan to extend this work by studying the evolving dynamics of the coalitions and the environment.

DRL Router: Distributional Reinforcement Learning-Based Router for Reliable Shortest Path Problems

This article studies reliable shortest path (RSP) problems in stochastic transportation networks. The term reliability in the RSP literature has many definitions, e.g., 1) maximal stochastic on-time arrival probability, 2) minimal travel time with a high-confidence constraint, 3) minimal mean and standard deviation combination, and 4) minimal expected disutility. To the best of our knowledge, almost all state-of-the-art RSP solutions are designed to target one specific RSP objective, and it is very difficult, if not impossible, to adapt them to other RSP objectives. To bridge the gap, this article develops a distributional reinforcement learning (DRL)-based algorithm, namely, DRL-Router, which serves as a universal solution to the four aforementioned RSP problems. DRL-Router employs the DRL method to approximate the full travel time distribution of a given routing policy and then makes improvements with respect to the user-defined RSP objective through a generalized policy iteration scheme. DRL-Router is 1) universal, i.e., it is applicable to a variety of RSP objectives; 2) model free, i.e., it does not rely on well calibrated travel time distribution models; 3) it is adaptive with navigation objective changes; and 4) fast, i.e., it performs real-time decision making. Extensive experimental results and comparisons with baseline algorithms in various transportation networks justify both the accuracy and efficiency of DRL-Router.

The effect of data science on urban sustainability through the optimization of demand-responsive transportation

The study investigated the viability of implementing a demand-responsive transportation (DRT) system within the premises of Al-Baha University to facilitate the punctual arrival of students to their classes and ensure their convenient departure from the campus. This study aims to demonstrate and elucidate how data mining enhances the efficiency of DRT systems. The case study investigates the potential improvements in DRT operations through the utilization of data mining techniques. The research employs descriptive and prescriptive techniques for mining trip planning data. The database maintained by the Deanship of Admission and Registration et al.-Baha University in Al-Baha City, Saudi Arabia, has identified 50 designated locations for student pick-up. The research establishes a comprehensive database that classifies pick-up points as spatial vectors, incorporating longitude, latitude, and counts of students. Cluster analysis and multidimensional scaling techniques are employed to minimize travel times and enhance operational efficiency by consolidating pick-up points. A completely adaptable DRT system prioritizes user satisfaction over operational efficiency, facilitating superior flexible services with fewer buses. The University Transport Department can implement Fixed Route systems in regions with significant population density and substantial demand. Conversely, in areas with a lower population, where Fixed Route systems may be less efficient, the department can opt for DRT systems. Additionally, the findings demonstrate the potential cost-saving benefits of implementing a hybrid system that integrates the university and off-campus student housing. The results indicate that providing fully flexible DRT services to students’ harms efficiency and user experience. A comparative analysis between the Fixed route system and the DRT system reveals that the latter has the potential to offer enhanced service to students, utilizing an equivalent number of vehicles while reducing the number of rides required, all while maintaining a higher degree of flexibility in service provision. According to the recommendation of this study, it is advisable to implement a hybrid fixed route and DRT system. However, to assess the feasibility of such a system in a university setting in the Kingdom of Saudi Arabia (KSA), it is necessary to conduct a comprehensive viability analysis to identify any potential challenges or concerns.

Timing co-evolutionary path optimisation method for emergency vehicles considering the safe passage

Emergency vehicles encounter traffic accidents at intersections of the network frequently, which seriously threatens the lives of rescue teams and patients. To improve the driving safety and travel efficiency of emergency vehicles, this study proposes a new method of timing co-evolutionary path optimisation for emergency vehicles based on the safety evaluation of intersections in an uncertain environment. First, the associated benefit calculation of emergency priority for vehicles passing through the nodes and road sections is provided then. Second, a mathematical description of the emergency vehicle path optimisation model is proposed, and its primary optimisation objective is to minimise travel time. A secondary optimisation objective based on a flexible time window is proposed to optimise the risk coefficient of the rescue path. Finally, an improved ripple-spreading algorithm is designed to find the optimal path. The findings can help to provide auxiliary decision support for the path selection of emergency rescue actions.

Non-Aggressive Adaptive Routing in Traffic

Routing a person through a traffic road network presents a tension between selecting a fixed route that is easy to navigate and selecting an aggressively adaptive route that minimizes travel time. In this paper, we propose a novel routing framework that strikes a balance between adaptability and simplicity. Specifically, we propose to create non-aggressive adaptive routes that seek the best of both these extremes in the navigation world. These selected routes still adapt to changing traffic conditions, but we limit the number of adjustments made en route. This framework improves the driver experience by providing a continuum of options between saving travel time and reducing navigation stress. We design strategies to model single and multiple route adjustments, and investigate numerous techniques to solve these models for better route selection. To alleviate the intractability of handling real-life traffic data, we devise efficient algorithms with easily computable lower and upper bounds. We finally perform computational experiments on our algorithms to demonstrate the benefits of limited adaptability in terms of reducing the travel time.

A Novel Vectorized Curved Road Representation Based Aerial Guided Unmanned Vehicle Trajectory Planning.

Unmanned vehicles frequently encounter the challenge of navigating through complex mountainous terrains, which are characterized by numerous unknown continuous curves. Drones, with their wide field of view and ability to vertically displace, offer a potential solution to compensate for the limited field of view of ground vehicles. However, the conventional approach of path extraction solely provides pixel-level positional information. Consequently, when drones guide ground unmanned vehicles using visual cues, the road fitting accuracy is compromised, resulting in reduced speed. Addressing these limitations with existing methods has proven to be a formidable task. In this study, we propose an innovative approach for guiding the visual movement of unmanned ground vehicles using an air-ground collaborative vectorized curved road representation and trajectory planning method. Our method offers several advantages over traditional road fitting techniques. Firstly, it incorporates a road star points ordering method based on the K-Means clustering algorithm, which simplifies the complex process of road fitting. Additionally, we introduce a road vectorization model based on the piecewise GA-Bézier algorithm, enabling the identification of the optimal frame from the initial frame to the current frame in the video stream. This significantly improves the road fitting effect (EV) and reduces the model running time (T-model). Furthermore, we employ smooth trajectory planning along the "route-plane" to maximize speed at turning points, thereby minimizing travel time (T-travel). To validate the efficiency and accuracy of our proposed method, we conducted extensive simulation experiments and performed actual comparison experiments. The results demonstrate the superior performance of our approach in terms of both efficiency and accuracy.

Logic-based benders decomposition for wildfire suppression

In this paper we consider a network interdiction problem where the follower is characterised by a spreading phenomenon. As a case study we consider in detail a wildfire suppression problem. The landscape is modelled as a directed graph, with nodes representing regions of the landscape, and arcs representing adjacency relationships. The fire spread is modelled using the minimum travel time principle. The goal is to locate fire suppression resources in a landscape in order to minimise the total area burned. As previous exact algorithms in this space are intractable on reasonable instances, the literature focuses on heuristics. In this paper we propose a new exact algorithm utilising logic-based Benders decomposition. We benchmark the new approach against mixed-integer programming and metaheuristic approaches. Our approach is able to quickly solve instances from the literature. We are also able to solve larger instances to optimality in a reasonable amount of time.

Modelling Method on Dynamic Optimal Setting and Associated Control for Intermittent Bus Lane

A new type of dynamic bus lane not only ensures the strategy of bus priority but also significantly improves the spatiotemporal utilization of road resources and reduces the conflicting demands of buses and social vehicles on road resources. Firstly, the heterogeneity of bus arrival time is analysed according to the process of aggregation and dissipation of vehicle queues at intersections. Considering the correlation between the operating states of social vehicles and buses, a dynamic control strategy for bus lane based on the insertable interval of social vehicles is established. Secondly, combining the setting conditions of the intermittent bus lane control area with the correlation scenario of signal control at intersections and then according to the HCM 2010 vehicle delay equation and the BPR function, the associated optimized control model with the minimum total travel time consumption of the road section as the objective function is constructed. The global optimal control of the intermittent bus lane is realized through the computational experiment. Finally, the setting conditions and benefits of three lane organization schemes (including intersect (i.e., no bus priority), parallel, and intermittent bus lanes) are compared and analysed through case study. The results indicate that the intermittent priority bus lanes have opening hours, which can not only ensure bus priority but also expand the right way for social vehicles and make full use of road space resources, thus improving the overall traffic efficiency.

Study on the Shortest Reliable Path of Stochastic Time-Dependent Transportation Networks considering Waiting Time at Signalized Intersections

Theoretical research is conducted on finding the shortest path with stochastic and time-dependent characteristics of link travel time in urban road networks. Considering the influence of signalized intersections on travel time, the research first presents a function of waiting time at signalized intersections and analyzes its characteristics. Then, considering the reliability of travel time, the travel time model under the min-max theorem is established, and a mathematical proof that the stochastic time-dependent traffic networks can be reduced to a deterministic time-dependent network is presented by using the first mathematical induction. Finally, based on analyzing the characteristics of the shortest path and minimum travel time, which vary with the start time, we propose solving the shortest path problem with a shortest path algorithm based on Dijkstra’s algorithm that takes the waiting time at signalized intersections into consideration. The research results showed that the algorithm proposed in this study does not depend on the acquisition of the probability distribution of travel time compared with the traditional algorithm. The range of uncertain travel time can be derived from historical data and travelers’ experiences, and the obtained shortest path has more robust time reliability.

Modeling Impacts of Implementation Policies of Tradable Credit Schemes on Traffic Congestion in the Context of Traveler’s Cognitive Illusion

A tradable credit scheme (TCS) is a novel traffic demand management (TDM) measure that can effectively mitigate traffic congestion in a revenue-neutral way. Under a given TCS, the cognitive illusion (CI) would occur when travelers instinctively use a specious thinking logic to estimate travel cost. The traveler’s CI would significantly influence his/her route choice behaviors, and thus the regulation effect of TCS on mitigating traffic congestion. To reveal the impacts of implementation policies of TCS on managing network mobility in the context of the traveler’s CI, this study investigated the traffic equilibrium assignment model with consideration of the traveler’s CI and the specific implementation policies of TCS. By incorporating the two types of factors into the generalized path travel cost (GPTC), the coupled user equilibrium (UE) and market equilibrium (ME) conditions are established to describe the equilibrium state of the traffic network under a given TCS. As the implementation policies of TCS are factored in the GPTC, different types of initial credit distribution scheme (ICDS) and the transaction costs (TC) of trading credits can be analyzed within the unified model framework. The coupled UE and ME conditions are then reformulated as an equivalent variational inequality (VI) model, and the sufficient conditions for the uniqueness of UE link flows and ME credit price are also provided. The system optimal (SO) TCS design problem is further investigated to achieve the minimum total travel time (TTT) of the transportation network, and two analytical methods are proposed to obtain the SO TCS in the context of the traveler’s CI. Numerical experiments are presented to verify the proposed model and methods. The results show that the presence of the traveler’s CI has an effect of lowering the ME credit price, and ICDS and TC have a complex network-wide influence on the ME credit price and UE link flows, which depends on the specific values of the relevant parameters.

The Utilization Of The Simple Multi Attribute Rating Exploiting Ranks Can Enhance The Performance Of The Aco Algorithm

In a comparative study, the performance of the ACO algorithm and a modified genetic algorithm (MGA) were evaluated for solving the multiple salesman traveling problem (MTSP) using various datasets from TSPLIB. The results revealed that although the proposed algorithm did not achieve the best solution, it exhibited improved time efficiency as the dataset size increased. The objective of this study is to improve the performance of the ACO algorithm by integrating the SMARTER algorithm, which aims to find the optimal route and minimize travel time. The combination of these algorithms offers alternative path solutions that can be effectively applied to solve TSP case examples and advance the development of new algorithms that excel in identifying the closest path. The study utilized TSPLIB datasets ranging in size from 76 to 1002 cities, sourced from the Felts and Nelson Krolak repositories. Within this study, the ACO algorithm was employed to optimize the overall distance in the TSP dataset, while the SMARTER algorithm generated suggestions for the optimal routes based on the total trip distance calculated by ACO. Experimental results demonstrated that the ACO algorithm, combined with SMARTER, achieved an average time improvement of 74.09% compared to the MGA algorithm, representing the most optimal performance.

The [formula omitted]-median problem with upgrading of transportation costs and minimum travel time allocation

In this paper, we analyse the upgrading of arcs in the p-median problem on a bi-network. Both travel times and transportation costs are associated with each arc. Our goal consists of simultaneously finding p medians, allocating each node to the median of minimum travel time and distributing a known budget among the arcs of the network to reduce their transportation cost, in order to minimise the total transportation cost of the system. The problem is motivated by the warehouse-to-locker structure of the distribution networks of many ecommerce businesses. We formulate it in two different ways as an integer programming problem, derive some properties of any optimal solution, develop valid inequalities and present computational results.

Surface Wildfire Regime and Simulation-Based Wildfire Exposure in the Golestan National Park, NE Iran

This research analyzes the spatiotemporal patterns of wildfire regime attributes (e.g., seasonality, size, frequency, and burn rate) across the Golestan National Park (GNP), northeast Iran over the last two decades. We used a variety of data, including existing vegetation data, current vegetation survey, and historical wildfire data, and then data were processed through ArcMap. We also predicted fire exposure profiles (burn probability (BP), conditional flame length (CFL (m)), and fire size (FS (ha)) by the application of the minimum travel time (MTT) fire spread algorithm. The kernel density estimation (KDE) method was used to estimate wildfire likelihood, based on recent wildfires (2000–2020) that occurred in the GNP. Finally, we developed a logistic regression model to investigate how independent variables such as weather, fuel, and topographic data influence wildfires in the park. Wildfires in the landscape have not been constant in either space or time. Their extent, seasonality, frequency, and other wildfire regime characters varied considerably across the landscape. Our results highlighted that shrublands in the southern part of the park showed, in general, the highest values in terms of the wildfire regime attributes. Large fires (10–100 ha, 51%) and very large fires (>100 ha, 24%), fire intervals greater than 10 years (90%), and high burn rates (>1% y−1, 35%) are all characteristics that contribute to high wildfire activity in shrublands. Similarly, areas predicted to have high wildfire exposure levels (average BP = 0.004; average CFL = 1.60 m; average FS = 840 ha) are found in the fuel models of high-load grass and medium-load shrub. Finally, the regression model results revealed that weather and fuel were the most influential parameters (R2 ≥ 0.2), while topography had comparatively less influence in the study area. In light of these results, we suggest proactively incorporating this information into fire and fuel management which can help develop a fire prevention plan, predict fire ignition probability and frequency, and finally address altered fire regimes threatening the park.

Learn global and optimize local: A data-driven methodology for last-mile routing

In last-mile routing, the task of finding a route is often framed as a Traveling Salesman Problem to minimize travel time and associated cost. However, solutions stemming from this approach do not match the realized paths as drivers deviate due to navigational considerations and preferences. To prescribe routes that incorporate this tacit knowledge, a data-driven model is proposed that aligns well with the hierarchical structure of delivery data wherein each stop belongs to a zone — a geographical area. First, on the global level, a zone sequence is established as a result of a minimization over a cost matrix which is a weighted combination of historical information and distances (travel times) between zones. Subsequently, within zones, sequences of stops are determined, such that, integrated with the predetermined zone sequence, a full solution is obtained.The methodology is particularly promising as it propels itself within the top-tier of submissions to the Last-Mile Routing Research Challenge while maintaining an elegant decomposition that ensures a feasible implementation into practice. The concurrence between prescribed and realized routes underpins the adequateness of a hierarchical breakdown of the problem, and the fact that drivers make a series of locally optimal decisions when navigating. Furthermore, experimenting with the balance between historical information and distance exposes that historic information is pivotal in deciding a starting zone of a route. The experiments also reveal that at the end of a route, historical information can best be discarded, making the time it takes to return to the station the primary concern.

MTTfireCAL Package for R—An Innovative, Comprehensive, and Fast Procedure to Calibrate the MTT Fire Spread Modelling System

Fire spread behavior models are used to estimate fire behavior metrics, fire hazard, exposure, and risk across the landscape. One of the most widely used fire spread models is the minimum travel time (MTT), which requires a very time-consuming, interactive, trial-and-error calibration process to reproduce observed fire regimens. This study presents the MTTfireCAL package for R, a tool that enables fast calibration of the MTT fire spread models by testing and combining multiple settings and then ranking them based on the model’s capacity to reproduce historical fire patterns, such as fire size distribution and fire frequency. Here, we explain the main methodological steps and validate the package by comparing it against the typical calibration procedures in two study areas. In addition, we estimate the minimum number of fire runs required to ensure a reliable calibration. Overall, the use of MTTfireCAL R package and the optimization of the number of ignitions used allowed for a faster calibration of the MTT modeling system than the typical trial-and-error calibration. The MTT modeling system calibrated using MTTfireCAL was also able to better reproduce the historical fire patterns. This tool has the potential to support the academic and operational community working with MTT.

Eco-Driving Optimization Based on Variable Grid Dynamic Programming and Vehicle Connectivity in a Real-World Scenario

In a context in which the connectivity level of last-generation vehicles is constantly on the rise, the combined use of Vehicle-To-Everything (V2X) connectivity and autonomous driving can provide remarkable benefits through the synergistic optimization of the route and the speed trajectory. In this framework, this paper focuses on vehicle ecodriving optimization in a connected environment: the virtual test rig of a premium segment passenger car was used for generating the simulation scenarios and to assess the benefits, in terms of energy and time savings, that the introduction of V2X communication, integrated with cloud computing, can have in a real-world scenario. The Reference Scenario is a predefined Real Driving Emissions (RDE) compliant route, while the simulation scenarios were generated by assuming two different penetration levels of V2X technologies. The associated energy minimization problem was formulated and solved by means of a Variable Grid Dynamic Programming (VGDP), that modifying the variable state search grid on the basis of the V2X information allows to drastically reduce the DP computation burden by more than 95%. The simulations show that introducing a smart infrastructure along with optimizing the vehicle speed in a real-world urban route can potentially reduce the required energy by 54% while shortening the travel time by 38%. Finally, a sensitivity analysis was performed on the biobjective optimization cost function to find a set of Pareto optimal solutions, between energy and travel time minimization.

Analyzing the trade-off between minimizing travel times and reducing monetary costs for users in the transit network design

The efficiency of the planning process in public transport is represented through different measures, which are practically impossible to optimize simultaneously. This study defines a bi-objective optimization problem for the transit network design to analyze the trade-off between minimization of travel times and reducing monetary costs for passengers (which was not addressed in the literature) while considering a hard constraint for operational costs. Indeed, the minimization of monetary costs for passengers is relevant in transport systems without a complete integrated fare system, where passengers may pay for each trip-leg; thus, modeling monetary costs for users is essential when referring to the system’s accessibility and route choice. To achieve our goal, we implement an epsilon-constraint algorithm capable of obtaining high-quality approximations of the Pareto front for benchmark instances in hours of computational time, which is reasonable for strategic planning problems. Numerical results show that the conflict between both objectives is evident, and it is possible to identify the more useful lines to optimize each objective, leading to relevant information for the decision-making process. Finally, we perform a sensitivity analysis on the budget parameter of our optimization problem, showing the classic trade-off between the operational costs and the level of service in terms of travel time and monetary cost.

Study of travellers' preferences towards travel offer categories and incentives in the journey planning context.

Nowadays, efforts to encourage changes in travel behaviour towards eco-friendly and active modes of transport are intensifying. A promising solution is to increase the use of sustainable public transport modes. Currently, a significant challenge related to this solution is the implementation of journey planners that will inform travellers about available travel solutions and facilitate decision-making by using personalisation techniques. This paper provides some valuable hints to journey planner developers on how to define and prioritise the travel offer categories and incentives to meet the travellers' expectations. The analysed data were obtained from a survey conducted in several European countries as part of the H2020 RIDE2RAIL project. The results confirm that travellers prefer to minimise travel time and stay on time. Also, incentives such as price discounts or class upgrades may play a crucial role in influencing the choices among travel solutions. By applying the regression analysis, it was found that preferences of travel offer categories and incentives are correlated with some demographic or travel-related factors. The results also show that subsets of significant factors strongly differ for particular travel offer categories and incentives, what underlines the importance of personalised recommendations in journey planners.

Patient Experience with Video Telemedicine Neurology Follow-up Visits at Community Clinics (P1-4.001)

<h3>Objective:</h3> To assess patient satisfaction and perception of physician empathy following implementation of video telemedicine neurology follow-up visits at community clinics <h3>Background:</h3> There is a growing shortage of neurologists in the US who provide care outside of metropolitan areas[1]. Within the region of our academic medical center, neurologists drive approximately 2 hours round-trip each day to provide care at community clinics. Telemedicine minimizes travel time for both patients and physicians while maintaining/improving access. We aimed to develop a telemedicine service and assess patient experience. <h3>Design/Methods:</h3> We implemented video conferencing with tablet for patient to interact with neurologist and a pan and tilt zoom camera attached to a moveable cart to enable neurologist to perform neurological exam. Telemedicine visits were offered to patients who had previously been seen face-to-face by the neurologist performing follow-up. Implementation was at a clinic 45 miles from tertiary center(site 1) between February 2020–January 2021 and at a clinic 43 miles from tertiary center(site 2) between July 2021–January 2022. After the telemedicine visit, a survey was completed by patient and/or caregiver, which included the consultation and relational empathy(CARE)[1] and telemedicine patient satisfaction measures(TPSM)[2]. <h3>Results:</h3> 31 patients from site 1 and 38 patients from site 2 participated in telemedicine follow-up visit and completed the survey. Mean age was 66 years. Follow-up reasons were Parkinson disease/parkinsonism(36.2%), migraine(20.3%), dementia(17.4%), epilepsy(14.5%), and other(11.6%). CARE measure mean responses indicated patients’ perception of relational empathy was excellent(65%), and very good(25%). TPSM mean responses were strongly agreed(71%) and agreed(28%) with regard to patient satisfaction. The neurologists involved anecdotally reported improved work-life balance. <h3>Conclusions:</h3> Patient satisfaction and perceived physician empathy was high for video telemedicine neurology follow-up at our community clinics, supporting expansion of this model. This model also improved work-life balance for the neurologist. Study of care outcomes and use for new patient consultations is needed. <b>Disclosure:</b> Dr. Truitt has stock in NVTA. Dr. Truitt has stock in QTRX. Dr. Truitt has stock in EXAS. Dr. Truitt has stock in ALC. Dr. Truitt has stock in ACAD. Dr. Truitt has stock in ABBV. Dr. Truitt has stock in NVS. Dr. Truitt has stock in VRTX. Dr. Truitt has stock in MRK. Dr. Truitt has stock in ATRI. Dr. Truitt has stock in HZNP. Dr. Truitt has stock in ZM. Dr. Arumaithurai has nothing to disclose. Dr. Young has nothing to disclose.

Factors associated with the closure of obstetric units in German hospitals and its effects on accessibility

BackgroundAn increase in regionalization of obstetric services is being observed worldwide. This study investigated factors associated with the closure of obstetric units in hospitals in Germany and aimed to examine the effect of obstetric unit closure on accessibility of obstetric care.MethodsSecondary data of all German hospital sites with an obstetrics department were analyzed for 2014 and 2019. Backward stepwise regression was performed to identify factors associated with obstetrics department closure. Subsequently, the driving times to a hospital site with an obstetrics department were mapped, and different scenarios resulting from further regionalization were modelled.ResultsOf 747 hospital sites with an obstetrics department in 2014, 85 obstetrics departments closed down by 2019. The annual number of live births in a hospital site (OR = 0.995; 95% CI = 0.993–0.996), the minimal travel time between two hospital sites with an obstetrics department (OR = 0.95; 95% CI = 0.915–0.985), the availability of a pediatrics department (OR = 0.357; 95% CI = 0.126–0.863), and population density (low vs. medium OR = 0.24; 95% CI = 0.09–0.648, low vs. high OR = 0.251; 95% CI = 0.077–0.822) were observed to be factors significantly associated with the closure of obstetrics departments. Areas in which driving times to the next hospital site with an obstetrics department exceeded the 30 and 40 min threshold slightly increased from 2014 to 2019. Scenarios in which only hospital sites with a pediatrics department or hospital sites with an annual birth volume of ≥ 600 were considered resulted in large areas in which the driving times would exceed the 30 and 40 min threshold.ConclusionClose distances between hospital sites and the absence of a pediatrics department at the hospital site associate with the closure of obstetrics departments. Despite the closures, good accessibility is maintained for most areas in Germany. Although regionalization may ensure high-quality care and efficiency, further regionalization in obstetrics will have an impact on accessibility.