Terms Of Travel Time Research Articles

Superblock Design and Evaluation by a Microscopic Door-to-Door Simulation Approach

The present study contributes to narrowing down the research gap in modeling individual door-to-door trips in a superblock scenario and in evaluating the respective impacts in terms of travel times, modal shifts, traffic performance, and environmental benefits. The methods used are a multiple-criteria approach to identify the superblocks and a large-scale, multi-model, activity-based microscopic simulation. These methods were applied to the city of Bologna, Italy, where 49 feasible superblocks were identified. A previous large-scale microscopic traffic model of Bologna is leveraged to build a baseline scenario. A superblock scenario is then created to model five proposed traffic intervention measures. Several mobility benefit indicators at both citywide and superblock levels are compared. The simulation results indicate a significant increase in walking time for car drivers, while the average waiting time of bus users decreases due to the increased frequency of bus services. This leads to a noticeable car-to-bus shift. In addition, absolute traffic volumes and traffic-related emissions decreased significantly. Surprisingly, traffic volumes on the roads around the superblocks did not increase as expected. In general, this research provides scientists and urban and transport planners with insights into how changes in door-to-door travel times of multi-modal trips can impact individual travel behavior and traffic performance at a citywide level. However, the study still has limitations in modeling the long-term effects regarding changing activity locations within the superblocks.

Spatial-temporal behaviour of hikers in the southeastern margin of Qinghai-Tibet Plateau: insights from volunteered geographic information

Understanding the macroscopic spatial-temporal behaviour of hikers plays an important role in targeted planning, marketing and management for regional hiking tourism development. The southeastern margin of Qinghai-Tibet Plateau, with abundant outdoor tourism resources and several World Heritage Sites, was chosen as the study area, and the development characteristics of hiking tourism and the spatial-temporal behaviour of its hikers were explored by seasonal intensity index, kernel density analysis, line density analysis and K-means clustering with volunteered geographic information (VGI). The results showed that hikers differed significantly from traditional tourists in terms of travel time, travel mode and destination choice, which provide vital implications for sustainable policy-making and tourism management in general and in the southeastern margin of Qinghai-Tibet Plateau in particular.

Quantification of Congestion and Level of Service Determination for Urban Road Infrastructure

Level of Service is an important measure for describing traffic condition in terms of travel time, speed, convenience, comfort, and safety. The aim of present study is to measure the congestion in mid-block sections using the corresponding average speed values and determine the LOS of the mid-block sections of urban roads based on v/c values. Three mid-block sections were chosen and video-recording method was used for collecting data. Based on the average speed values the level of congestion was determined and the level of service of each mid-block section was also determined by using the v/c ratios. No/low level of congestion was observed in Section- I and Section-III whereas moderate congestion was observed in Section-II. As a result of congestion, fuel consumption will be more, which in turn causes air pollution. When the dynamic PCU values were used for v/c determination, the LOS was A in all the sections.

A multi-objective evolutionary approach for the electric vehicle charging stations problem

This paper presents a framework that integrates a mobility and traffic simulator with evolutionary algorithms to solve the location problem of electric vehicle charging infrastructure. The framework presented in this research is configured with a real-life scenario that considers a maximum number of charging stations and a specific number of electric vehicles that are driven according to a movement distribution model for the urban city under consideration. The concerning scenario is formulated as a multi-objective optimization problem and is approximated by two multi-objective evolutionary algorithms based on two evolutionary approaches for multi-objective optimization, namely, NSGA-II and MOEA/D-gen. In this way, the adopted algorithms search for optimal station locations and capacities in terms of travel time and the number of charging stations. The analysis of the results obtained by the multi-objective approaches used in the proposed framework is two-fold. On the one hand, solutions confirm that both objectives considered in this study are in conflict. That is, the better the solution for one objective is, the more deteriorated it is for the other. On the other hand, it was found that NSGA-II obtained a better approximation to the optimal solutions according to two multi-objective performance indicators. These results give an idea of the type of algorithms that could best solve the formulated problem in terms of efficiency and effectiveness and understand the behavior of evolutionary algorithms in the multi-objective electric vehicle charging stations problem. Therefore, this research traces the route to study different scenarios and analyze the characteristics of the problem. Furthermore, it opens the door to exploring other meta-heuristics and their components to deal with this problem adequately.

A Study of Roundabout Sustainability using Traffic Simulation -A Case Study at Ayer Hitam Signalised Intersection

Through urban planning and municipal administration, a sustainable city seeks to solve issues of social, environmental, and economic effect. By integrating environmentally friendly options into local infrastructure, many sustainable efforts are made possible. Vehicle emissions from the road traffic have always been considered one of the most significant sources of global issues due to their harmful effects on the environment and human beings. Additionally, it is currently a concern for sustainability, especially in urban areas. This matter has inspired the researchers to simulate various systems to identify factors and provide solutions for the issue of emission. In this study, VISSIM software was utilised to develop a traffic simulation to estimate emissions level at Ayer Hitam’s signalised intersection in reference to the intersection type as an independent factor. The signalised intersection and a roundabout were chosen to represent controlled and uncontrolled intersections. It aimed to compare the difference in emissions level between the signalised intersection and roundabout. The results of this study show that roundabouts are more effective in enhancing traffic flow than signalised intersections in terms of travel time, delay, queueand have 48.59% lower for (CO), (NOx) and (VOC) emission. An improvement in vehicleemissions results from thisstudy indicates that roundabouts have the potential to contribute to a more sustainabletransportation system and sustainable city.

A branch-cut-and-price algorithm for the time-dependent electric vehicle routing problem with time windows

The adoption of electric vehicles (EVs) within last-mile deliveries is considered one of the key transformations towards more sustainable logistics. The inclusion of EVs introduces new operational constraints to the models such as a restricted driving range and the possibility to perform recharges en route. The discharge of the typical batteries is complex and depends on several variables, including the vehicle travel speed, but most of the approaches assume that the energy consumption depends only on the distance traveled. This becomes relevant in different logistics contexts, such as last-mile distrubtion in large cities and mid-haul logistics in retail, where traffic congestion affects severely the travel speeds. In this paper, we introduce a general version of the Time-Dependent Electric Vehicle Routing Problem with Time Windows (TDEVRPTW), which incorporates the time-dependent nature of the transportation network both in terms of travel times and the energy consumption. We propose a unifying framework to integrate other critical variable times arising during the operations previously studied in the literature, such as the time-dependent waiting times and non-linear charging times. We propose a state of the art branch-cut-and-price (BCP) algorithm. Based on extensive computational experiments, we show that the approach is very effective solving instances with up to 100 customers with different time dependent configurations. From a managerial standpoint, our experiments indicate that neglecting the travel speeds can affect the quality of the solutions obtained, where up to 40 percent of the infeasibilities induced by neglecting the time dependency can be caused by exceeding the battery capacity.

Optimization of ecological sports innovation teaching path based on multiple intelligence evaluation model

Abstract Rural sports tourism is an emerging industry that integrates rural tourism and sports tourism and plays an important role in broadening the field of sports and cultural consumption and driving rural economic development. Under the background of big data, how to better integrate rural sports culture and sports tourism becomes an important issue to improve the quality of rural tourism. This paper analyzes the big data of rural sports tourism on the Internet review platform based on big data processing technology, combined with the investigation and research on the current situation of rural sports tourism resources in a town. The big data analysis shows that rural sports tourists are only 7% more female than male, and the overall gender ratio is relatively balanced. In terms of age composition, the largest number of tourists were between 36 and 45 years old, accounting for 42.6%. In addition, rural sports tourists aged 26 to 60 accounted for 82.6% of all, indicating that rural sports tourism is dominated by middle-aged and elderly people. In terms of travel time, the largest number of rural sports tourists chose 2 to 3 days, accounting for 33.8%, and only 2.3% traveled for more than 7 days. In the choice of travel mode, 37.6% of tourists are organized by travel agencies, accounting for the largest number of travelers. From the big data emotional analysis of the evaluation words of sports tourists, the most frequent occurrence is characteristic, reaching 435 times, and the least frequent is danger, but also 89 times. Meanwhile, convenience and health also appear more frequently. Comprehensive analysis of the above, this paper believes that rural sports tourism should promote more local characteristics of sports culture and highlight differentiation while strengthening personal safety protection measures for tourists.

Anatomy of perturbed traffic networks during urban flooding

Urban flooding disrupts traffic networks, affecting mobility and disrupting residents’ access. Flooding events are predicted to increase due to climate change; therefore, understanding traffic network’s flood-caused disruption is critical to improving emergency planning and city resilience. This study reveals the anatomy of perturbed traffic networks by leveraging high-resolution traffic network data from a major flood event and advanced high-order network analysis. We evaluate travel times between every pairwise junction in the city and assess higher-order network geometry changes in the network to determine flood impacts. The findings show network-wide persistent increased travel times could last for weeks after the flood water has receded, even after modest flood failure. A modest flooding of 1.3% road segments caused 8% temporal expansion of the entire traffic network. The results also show that distant trips would experience a greater percentage increase in travel time. Also, the extent of the increase in travel time does not decay with distance from inundated areas, suggesting that the spatial reach of flood impacts extends beyond flooded areas. The findings of this study provide an important novel understanding of floods’ impacts on the functioning of traffic networks in terms of travel time and traffic network geometry.

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.

MP12-14 RURAL DISPARITIES IN PENILE CANCER: A MULTI-INSTITUTIONAL, STATEWIDE REVIEW IN WEST VIRGINIA

You have accessJournal of UrologyCME1 Apr 2023MP12-14 RURAL DISPARITIES IN PENILE CANCER: A MULTI-INSTITUTIONAL, STATEWIDE REVIEW IN WEST VIRGINIA David Zekan, Luke O'Connor, Mary Novak, Amr Elbakry, Brian Wiseman, Ahmad Haffar, Andrew Williams, Nathan Hale, Adam Luchey, and Ali Hajiran David ZekanDavid Zekan More articles by this author , Luke O'ConnorLuke O'Connor More articles by this author , Mary NovakMary Novak More articles by this author , Amr ElbakryAmr Elbakry More articles by this author , Brian WisemanBrian Wiseman More articles by this author , Ahmad HaffarAhmad Haffar More articles by this author , Andrew WilliamsAndrew Williams More articles by this author , Nathan HaleNathan Hale More articles by this author , Adam LucheyAdam Luchey More articles by this author , and Ali HajiranAli Hajiran More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003227.14AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Disparities exist amongst HPV-related cancer outcomes in urban versus rural populations, including squamous cell carcinoma (SCC) of the penis. West Virginia presents a unique opportunity for analysis in underserved areas with few tertiary care centers and large distances and travel time between centers. This study seeks to examine differences in penile cancer presentation (including TNM stage, grade, pertinent risk factors, presenting symptoms and duration), treatment, and outcomes between rural and urban populations in West Virginia. Identification is crucial to provide earlier intervention and improve outcomes in an underserved population. METHODS: Following IRB approval, 89 patients with biopsy-proven SCC of the penis treated at Charleston Area Medical Center (CAMC) and West Virginia University over a fourteen-year period (2007-2022) were studied retrospectively. Distance and travel time from care centers were collected, and patients were stratified into urban and rural groups according to the US Census Bureau definition. T-stage, clinical node positivity, grade, risk factors, primary tumor size and location, and treatment modality and timing were analyzed. Appropriate statistical tests were performed using SPSS. RESULTS: Patients living increased travel times from the respective centers were more likely to undergo radical penectomy as primary therapy (p=0.03), although advanced stage (pT2 or greater) and clinical nodal status did not vary with travel time. Those living in counties classified as “urban” had higher BMI on average (p=0.004) and were more likely to present with a lesion on the penile shaft (p=0.06). No difference exists in stage or grade at diagnosis, other risk factors (smoking status, circumcision), primary tumor size, clinically palpable lymph nodes, radiation and chemotherapy, symptom duration, or timing of therapy based on distance or travel time or when stratified based on rural/urban county of residence. CONCLUSIONS: Patients further from tertiary care centers in terms of travel time are more likely to undergo radical penectomy as definitive therapy for penile cancer. This was not significant when analyzed by distance from tertiary care center, as may be expected in a rural state where distance and travel time can vary markedly. Also, this did not occur due to higher risk disease based on pT stage. When stratified based on census definitions, rural patients had lower BMI and presented less often with disease of the penile shaft, suggesting they may opt for a definitive radical penectomy due to difficulties with healthcare access. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e139 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information David Zekan More articles by this author Luke O'Connor More articles by this author Mary Novak More articles by this author Amr Elbakry More articles by this author Brian Wiseman More articles by this author Ahmad Haffar More articles by this author Andrew Williams More articles by this author Nathan Hale More articles by this author Adam Luchey More articles by this author Ali Hajiran More articles by this author Expand All Advertisement PDF downloadLoading ...

Proportional Estimation Method of Urban Rail Passenger Flow Transfer Path Selection Based on IC Card Data

Based on the analysis of the influencing factors of urban rail transit passenger flow transfer path selection behavior and the components of travel time, a method of estimating the proportion of urban rail transit passenger flow transfer path selection based on IC card data is proposed. Firstly, the research determines the shortest interchange path algorithm as Dijkstra algorithm and the graph-based depth-first search algorithm as the effective interchange path search algorithm; analyzes the factors influencing the passenger flow interchange path selection behavior as three types of travel time, interchange cost and road network familiarity; constructs the generalized cost function of effective interchange path selection by combining travel time and interchange cost; establishes the passenger flow probability selection model by combining road network familiarity. To eliminate the influence of absolute difference of utility on the probability of path selection in the polynomial logit model, the path probability selection model is improved with the help of minimum travel cost; Bayesian estimation is used to compare the passenger travel time with the theoretical travel time to identify the closest effective interchange path, and the joint distribution probability is used to calibrate the probability selection model in terms of travel time and interchange cost. The coefficient, coefficient of interchange passenger flow, penalty coefficient of interchange number, familiarity of road network and other related parameters are combined with the joint distribution probability to calibrate the probability of selection of different effective interchange paths.

Use of GPS and self-reported data to evaluate daily trips and the impact of travel information

Daily trips in medium-sized cities are based more on habitual patterns, resulting sometimes in less optimal route choices. In this study, an attempt is made to investigate trips in a medium-sized city and evaluate them to explore the impact of travel information. To achieve that, a survey of two phases was performed. First, self-reported and GPS data of an examined group of 96 participants of the University of Thessaly, Volos, Greece was collected. The data was used to evaluate the daily trips in terms of travel time, cost, and environmental friendliness. Second, a stated preference survey was designed targeted at motorized vehicle users of the examined group. The survey investigated the extent that shared information on social media can be used to recommend a different route than the usual one or convince them to shift to a sustainable way of transportation.

Specific cases of zone-oriented timetable

This paper deals with the topic of railway zone-oriented timetable which is an operational concept suitable for suburban and alternatively long-distance transport. Initially, the paper describes the main principles, advantages, and disadvantages of the zone-oriented timetable, especially in terms of travel times, train sets capacity, and railway capacity. Then there are described some specific cases/forms of the zone-oriented timetable and compared to the basic form of the zone-oriented timetable in the mentioned terms. Also, some examples of the concepts, where they are or can be applied, are stated.

Different Path Planning Techniques for an Indoor Omni-Wheeled Mobile Robot: Experimental Implementation, Comparison and Optimization

Omni-wheeled mobile robots (Omni WMRs) are commonly used in indoor navigation applications like surveillance, search and rescue, and autonomous transportation. They are always characterized by their versatility, mobility and high payload. This paper presents the mechatronic design, low-level control and high-level control of an indoor 4 Omni-Wheeled Mobile Robot (4OWMR). Since autonomy and path planning are research necessities for WMRs, four heuristic and probabilistic path-planning techniques are chosen for experimental implementation. The selected techniques are PRM (Probabilistic Roadmaps), RRT (Rapidly exploring Random Tree), RRTSTAR (RRT*), and ASTAR (A*) algorithms. The proposed environments are static, expressed by maps with unknown nodes and obstacles. Local path planning is implemented with simultaneous localization and mapping (SLAM). Path planning techniques are programmed, and the obtained paths are optimized by a multi-objective genetic algorithm technique to ensure the shortest path and its smoothness. The optimized paths are deployed to the 4OWMR. The obtained results are compared in terms of travel time, travel distance, average velocity and convergence error. A ranking technique is utilized to rank the obtained results and show the most preferred technique in terms of energy consumption and convergence accuracy in addition to the overall ranking. Experimental results showed that the Hybrid A* algorithm produced the best-generated paths with respect to other techniques.

Transport vulnerability: Measuring travel time and expenditure budget in Addis Ababa

Public transport is critical for urban dwellers as it is a key link to access economic opportunities and social services. However, transit services in most developing nations are characterized by longer journey time, high fare, unsafe and uncomfortable travel. Hence, this study seeks to analyze the travel experience of public transit users through the lens of vulnerability. The main objective of the research is to investigate transit users’ expenditure on travel, both in terms of time and money, which are regarded as “budgets” allocated to travel. The analysis is based on a survey distributed to 457 public transit users in Addis Ababa. The study applied proportions, percentages and cross-tabulation of key variables and statistical methods, such as one-way ANOVA to analyze the level of vulnerability of transit users. The result shows that low- and middle-income groups bear a higher level of travel burden, in terms of total travel time and percentage of income spent on transport than high income groups. Transport expenditure accounted above 20% of household income for 88% of low-income groups. However, it is below 15% of household income for high income groups, which reveals less financial burden on them. In terms of travel time, 63.8% of middle income groups experience a total travel time of more than 2 h and 52.3% of low-income groups but only 36.3% of high-income groups devote a total travel time above 2 h. Therefore, transport policy formulation should consider the needs and challenges of various groups of transit users.

Strategic Transit Route Recommendation Considering Multi-Trip Feature Desirability Using Logit Model with Optimal Travel Time Analysis

Route recommendation continues to manifest noteworthy contributions to the intelligent transportation system field of research as it evolves through time. Early related studies helped passengers and tourists experience a more convenient travel. At the same time, these helped transport planners analyze people’s trip preferences and its correlation with the region-specific economic status in a more time-relevant data. Majority, however, require historical data and heavy data collection methods. For user quantified metrics such as route cost in terms of travel time and distance, the complexity and sparsity of preferences between travelers are persistent challenges. The strategic transit route recommendation proposed in this study takes into account multiple trip features (both quantitative and qualitative) desirability using logit model and the optimal travel time with respect to a given road traffic condition, headway, and passenger demand. The chosen area of study is the Western Visayas region of the Philippines specific to the public utility bus (PUB) and jeepney (PUJ) transit routes. The results of the research exhibited the feasibility of an optimal and strategic recommendation of public transportation route for passengers considering present time relevant trip conditions rather than relying on the historical data which are difficult to obtain, or worse, non-existent.

A model-based evolutionary algorithm for home health care scheduling

In this paper, for the first time a model-based evolutionary algorithm is presented for a real-life Home Health Care Routing and Scheduling Problem (HHCRSP). The algorithm generates routes consisting of care activities jointly with the underlying shift schedule, while taking into account the qualification levels. The performance is optimized in terms of travel time, time window waiting time and shift overtime. The algorithm is a novel extension of the permutation Gene-Pool Optimal Mixing Evolutionary Algorithm. Numerical experiments, using real-life data, show that the algorithm performs close to optimal for small instances, and outperforms schedules from a case study, leading to efficiency gains of 41%. Furthermore, it is shown that the model-based evolutionary algorithm performs better than a more traditional evolutionary algorithm, which demonstrates the importance of learning and exploiting a model to guide the optimization in HHCRSP.

Data analytics for improved closest hospital suggestion for EMS operations in New York City

In addition to fires and various other emergencies, the Fire Department of New York City (FDNY) responds to medical emergencies throughout New York City. When a patient requires hospital care, the FDNY uses a hospital suggestion pattern to recommend the closest hospital that can appropriately treat the patient, given the geographic location of the incident. In a city like New York with a high density of hospitals, determining the closest hospital (in terms of travel time) is not trivial. This paper presents the results of a collaboration between the FDNY and Columbia University which integrates data analytics in the derivation, implementation and monitoring of this hospital suggestion pattern. Both telematics data of city-owned vehicles and historical ambulance travel times to hospitals are utilized to estimate the order of closest hospitals for a given incident location. Implementation constraints (coarse spatial and temporal resolutions of the pattern) and sparsity of the historical ambulance dataset generate large aleatory and epistemic uncertainties that are accounted for using a probabilistic data fusion procedure. The result of this analysis, hospital suggestion pattern N, was implemented in December 2020. A statistical analysis shows an overall decrease in travel times to hospitals as an effect of this pattern. • A data-driven methodology is used to optimize hospital transports in New York City. • The methodology leverages a road network and origin–destination ambulance data. • Uncertainties in travel times are accounted for in the data fusion procedure. • The proposed solution was successfully deployed in December 2020 in New York City.

Deriving transport appraisal values from emerging revealed preference data

Transport demand models are widely used to inform policy making and produce forecasts of future demand. A core output derived from demand models is the Value of Travel Time (VTT), which provides insights on the trade-offs that travellers are willing to make in terms of travel time and travel cost. VTT estimates are a critical input to cost-benefit analyses and feasibility assessments of potential projects and thus play a crucial role in transport planning and policy decisions. While much of the early work on VTT made use of revealed preference (RP) data, their use decreased due to growing concerns about reporting errors that may result in omitted observations and measurement errors in the model inputs. As a consequence, VTT measures have, for the last two decades, primarily been estimated using state-preference (SP) surveys. While SP methods can assess the individual trade-offs in a controlled manner, they are prone to behavioural incongruence. More recently, RP data from passively-collected data sources have raised the promise of accounting for some of the limitations of traditional RP surveys due to the minimal (or even no) active input from the respondent. The present study utilises such a dataset that combined a 2-week trip diary captured through smartphone GPS tracking with a household survey containing individual socio-demographic information. A mixed Logit model for mode choice was specified and the estimated parameters were then applied on the National Travel Survey to calculate the VTT estimates. Those estimates were further adjusted based on trip distances to get more representative national VTT values. This process resulted in estimates similar to the official UK guidelines used in transport appraisal that were obtained from SP data, where our results are not affected by concerns about response quality or survey artefacts. The findings hence strengthen the case for shifting towards passively generated RP data sources and are important for transport practitioners.

High-Speed Railway Network Development, Inter-County Accessibility Improvements, and Regional Poverty Alleviation: Evidence from China

The rapid expansion of the high-speed railway (HSR) network in China has significantly shortened the space–time distance between cities. China is striving to enter an anti-poverty era, which is increasing the importance of research on the poverty reduction effect created by upgrading transportation infrastructure, in particular, HSR development. Describing the characteristics of accessibility and the mechanisms by which that accessibility reduces poverty could provide the insights needed for determining suitable anti-poverty paths. By using data for 2341 counties and equivalents in China during 2007–2018, this study analyses the railway accessibility improvements and the poverty reduction effect created by HSR development. On average, HSR in China contributed to a significant increase in potential economic accessibility (317.8%) and a decrease in weighted average travel time (39.9%) for counties. Based on accessibility calculations, the Theil index was used to measure the disparity level of regional accessibility and regional poverty measured based on the income of rural residents. The results indicate that HSR leads to an increase in inequality in terms of travel time and potential economic accessibility at a national level. Pearson coefficients reveal a strong correlation between disparities in accessibility and in rural income among provinces. Furthermore, using the full sample, and sub-samples of poor and non-poor counties in China, the association between regional accessibility and poverty was examined by using two-way fixed effect models and spatial econometric models. The estimated results show that a 1% improvement in potential economic accessibility leads to an aggregate rural income improvement of 0.03–0.17%; the ratio of rural income to urban income increases by 0.04–0.12% and a larger effect is observed in poor counties. The weighted average travel time reduction also leads to improvement in rural income and reduction in the urban–rural income gap. The empirical results obtained by different robust test methods, including different sample groups, different estimated methods and accessibility indicators, are shown to be robust. These findings can help transportation departments formulate poverty-alleviation-oriented transportation planning and investment policies and inform future policies for countries planning to construct HSRs.