Road Travel Research Articles

Проблемы доступности и комфортности объектов туристической инфраструктуры для развития автомобильного туризма в России

This study examines the potential for the development of automobile tourism in Russia and its surrounding difficulties, focusing on the convenience and accessibility of tourist facilities. The analysis conducted includes a thorough dissection of the theoretical framework and consideration of the practical elements of the industry. The main objective is to improve and modernize road travel conditions in the country through a comprehensive approach. The paper analyzes existing data and conducts a comprehensive assessment that considers the many factors that influence road tourism. Key problems identified include limited development of the road network, regional differences in infrastructure and a lack of quality services in remote parts of Russia. The challenges faced by international tourism are also raised, including strict visa and customs procedures that prevent foreign road tourists from visiting Russia. It is highlighted that solving these problems will require efforts on the part of both the government and the private sector, in the form of partnership programs to improve infrastructure and services. Specific measures are proposed to develop the transportation network, improve road safety, stimulate rural and ecotourism, and support small businesses in tourism. At the same time, the integration of the latest technologies and the establishment of international tourism are emphasized. Emphasis is placed on the importance of environmental considerations for sustainable tourism development, given the growing interest in road travel in Russia. This study aims to develop strategies that can lead to the successful development of automobile tourism, enriching the economic and cultural life of the country.

Fit for 2030? Possible scenarios of road transport demand, energy consumption and greenhouse gas emissions for Italy

Decarbonizing the transportation sector is one of the key challenges to reduce greenhouse gas (GHG) emissions and meet EU Green deal 2030 targets. Road transport decarbonization requires a holistic approach encompassing various strategies and interventions aimed at fostering the sustainable transition of this sector. This challenge is crucial given that road is responsible for over 90% of the overall transport GHG emissions.Within this framework, this study aims, for the Italian case, to: a) estimate the national passenger and freight road travel demand with respect to three different time periods: 2005 (reference year for EU “fit for 55” target); 2019 (last consolidated pre-covid year); 2022 (base-line year); b) define possible road transport scenarios to 2030 following the Avoid-Shift-Improve (ASI) approach; c) estimate the GHG and energy consumption inventory related to national road transport for the 2005, 2019; 2022 and 2030 scenarios. Due to the current “deep uncertainty” regarding key economic and social variables in this period, two forecasting scenarios – named “moderate decarbo” and “high decarbo” – were designed and based on different and somewhat opposite hypotheses on exogenous variables (e.g., GDP) and penetration rates of measures and policies promoting sustainable transportation (e.g., subsidies for electric vehicles) already in place. Although the current European legislation refers only to Tank-to-Wheel (TtW) objectives, this study included also the Well-to-Wheel (WtW) evaluation, which is more accurate in assessing the GHG impact of different policies, energy carriers, and technologies. As for the past, results suggest that GHG reductions over the period 2005–2022 are mostly due to crises and stagnation of the Italian economy. “Moderate decarbo” forecasts for 2030 lead to a 12% reduction of TtW (and 15% for WtW) GHG emissions compared to 2005 levels, with ASI policies compensating for the assumed economic growth. In any case these values are far from the EU “Fit for 55” goal setting a 43.7% reduction of TtW emissions. Even under very favourable hypotheses (“high decarbo” scenario), GHG emissions might be reduced by only 28% for TtW and 33% for WtW, still well below EU objectives. Furthermore, passengers and freight show very different behaviour, with the latter contributing to emissions more than proportionally, and “shift-to-rail” policies, although very useful in some market segments, contribute moderately at the overall national level. The main conclusion of this study is that the pathway towards road transport decarbonization in Italy (and possibly in other EU Countries) is very uncertain, and the ambitious 2030 target is unlikely to be met with currently planned policies alone. The transportation system needs to be constantly monitored, and new policies have to be implemented to reach higher emissions reduction goals.

Computed Tomography Evaluation of Coronary Atherosclerosis: The Road Travelled, and What Lies Ahead.

Coronary CT angiography (CCTA) is now endorsed by all major cardiology guidelines for the investigation of chest pain and assessment for coronary artery disease (CAD) in appropriately selected patients. CAD is a leading cause of morbidity and mortality. There is extensive literature to support CCTA diagnostic and prognostic value both for stable and acute symptoms. It enables rapid and cost-effective rule-out of CAD, and permits quantification and characterization of coronary plaque and associated significance. In this comprehensive review, we detail the road traveled as CCTA evolved to include quantitative assessment of plaque stenosis and extent, characterization of plaque characteristics including high-risk features, functional assessment including fractional flow reserve-CT (FFR-CT), and CT perfusion techniques. The state of current guideline recommendations and clinical applications are reviewed, as well as future directions in the rapidly advancing field of CT technology, including photon counting and applications of artificial intelligence (AI).

A two-context-aware approach for navigation: A case study for vehicular route recommendation

In contemporary urban environments, route recommendation systems have become an indispensable tool in moving the population from large centers, serving as valuable resources for circumventing traffic congestion. Enhancing vehicular traffic flow through strategic route adjustments is a pivotal element in improving traffic mobility. However, depending exclusively on traffic-related data for route recommendations fails to meet the essential criteria for ensuring effective management and safety for drivers and passengers during travel. Thus, context awareness and traffic data are crucial for enhancing efficiency and safety in traffic management. Our study proposes a two-context-aware approach to recommend safe routes for urban traffic management, considering road safety and travel time. Experiments were carried out using the widely recognized tool — HERE Navigation. Comparatively, our approach signifies a progressive stride in balancing mobility and security when contrasted with a single focus on travel time.

The Impedance of East Jakarta Road Network to Estimate Fire Station Service Areas

Urban fires, prevalent in densely populated areas, pose significant risks by increasing deaths and injuries. Fire departments must navigate challenging access routes to manage these incidents effectively. This study analyzes the service coverage of fire stations in East Jakarta, considering road network, width, speed, and travel time. The study utilizes secondary data from the DKI Jakarta Provincial Fire and Rescue Service, road network data from local government sources, and traffic data from Google Maps. Additionally, a field survey was conducted to validate road conditions and accessibility. Using graph theory-based network analysis, the study assesses connectivity, flows, directions, and destinations to determine the coverage extent. The optimal route, defined by road class, width, and condition, exhibits the lowest impedance. Google Maps’ estimated travel times, incorporating traffic conditions, are used to assess travel times, with a 5-minute travel time set as the standard barrier for coverage. Results reveal that the current service coverage of East Jakarta fire stations is only 66.57%. This disparity indicates an inadequate number of fire stations relative to their required service areas. The findings underscore the need for strategic placement of additional fire stations and potential improvements in road infrastructure to enhance response times. Traffic dynamics often affect travel times, demonstrating that shorter distances do not always result in faster arrivals according to real-time data.

A two-stage road sign detection and text recognition system based on YOLOv7

We developed a two-stage traffic sign recognition system to enhance safety and prevent tragic traffic incidents involving self-driving cars. In the first stage, YOLOv7 was employed as the detection model for identifying 31 types of traffic signs. Input images were set to 640 × 640 pixels to balance speed and accuracy, with high-definition images split into overlapping sub-images of the same size for training. The YOLOv7 model achieved a training accuracy of 99.2 % and demonstrated robustness across various scenes, earning a testing accuracy of 99 % in both YouTube and self-recorded driving videos. In the second stage, extracted road sign images underwent rectification before processing with OCR tools such as EasyOCR and PaddleOCR. Post-processing steps addressed potential confusion, particularly with city/town names. After extensive testing, the system achieved recognition rates of 97.5 % for alphabets and 99.4 % for Chinese characters. This system significantly enhances the ability of self-driving cars to detect and interpret traffic signs, thereby contributing to safer road travel.

Impacts of brightness contrast, road environment complexity, travel direction and judgement type on speed perception errors among older adult pedestrians' road-crossing decision-making.

This study aimed to explore how various factors affect older people's vehicle speed perception to enhance their road safety as pedestrians, focusing on the impact of their cognitive and perceptual abilities on road-crossing decisions. The study evaluated the effects of brightness contrast (high, medium and low), road complexity (high and low) and vehicle travel direction (same and opposite) on speed perception errors in simulated traffic settings. It involved 38 older participants who estimated the speed of a comparison vehicle under two judgement conditions. Findings showed a consistent underestimation of speed in all conditions. A repeated-measure ANOVA revealed that speed perception errors were significantly higher with low brightness contrast, in simpler road environments, with vehicles travelling in the same direction, and when using absolute judgements. These results have practical importance for public safety initiatives, traffic regulation and road design catering to older adults' perceptual needs. They also provide valuable insights for driver training programs for older adults, aimed at enhancing their understanding and management of perceptual biases.

Mobility and Public Transport in Post-independence Mozambican Fiction (1992-2022)

ABSTRACT This article analyzes representations of mobility and public transport in the following Mozambican novels: Terra Sonâmbula [Sleepwalking Land] (1992), O Outro Pé da Sereia [The Mermaid’s Other Foot] (2006) by Mia Couto, O Comboio de Sal e Açúcar [The Train of Salt and Sugar] (1999) by Licínio Azevedo, and Museu da Revolução [Museum of the Revolution] (2022) by João Paulo Borges Coelho. Despite the importance of mobility and public transport in these works, existing scholarship has not considered these themes but has opted for more traditional categories such as ‘travel’, ‘diaspora’, and ‘migration’. Focusing on the literary portrayals of public transport and infrastructure of mobility – i.e. buses, ships, and railway stations (Couto), a slow-moving train (Azevedo), and a Toyota Hiace car (Borges Coelho) – this article aims to demonstrate the central role that the mobility/immobility binomial plays in the representation of post-independent Mozambique. The main argument of this article is that the images of the railway, the road, automobility, and maritime travel are the literal driving force of the narratives and contribute to the (un)building of the national space. I use literary perspectives on mobility studies and world-system approaches developed within the framework of world-literature (Warwick Research Collective) to demonstrate that the tropes of mobility and the representations of public transport in the four novels register and encode the social, political, and economic transitions in Mozambique’s colonial and post-colonial history and its incorporation into the capitalist world-system.

Winter Storm Severity Index in Alaska: Understanding the Usefulness for Impact-Based Winter Weather Severity Forecast Information

Abstract There is growing interest in impact-based decision support services to address complex decision-making, especially for winter storm forecasting. Understanding users’ needs for winter storm forecast information is necessary to make such impact-based winter forecasts relevant and useful to the diverse regions affected. A mixed-method social science research study investigated extending the winter storm severity index (WSSI) [operational for the contiguous United States (CONUS)] to Alaska, with consideration of the distinct needs of Alaskan stakeholders and the Alaskan climate. Data availability differences suggest the need for an Alaska-specific WSSI, calling for user feedback to inform the direction of product modifications. Focus groups and surveys in six regions of Alaska provided information on how the WSSI components, definitions, and categorization of impacts could align with stakeholder expectations and led to recommendations for the Weather Prediction Center to consider in developing the WSSI Alaska product. Overall, wind (strength and direction) and precipitation are key components to include. Air travel is a critical concern requiring wind and visibility information, while road travel is less emphasized (contrasting with CONUS needs). Special Weather Statements and Winter Storm Warnings are highly valued, and storm trajectory and transition (between precipitation types) information are the important contexts for decision-makers. Alaska is accustomed to and prepared for winter impacts but being able to understand how components (wind, snow, and ice) contribute to overall impact enhances the ability to respond and mitigate damage effectively. The WSSI adapted for Alaska can help address regional forecast needs, particularly valuable as the climate changes and typical winter conditions become more variable. Significance Statement Impact-based support services can assist decision-makers in prioritizing preparedness and mitigation actions related to winter storm events. The winter storm severity index adapted for specific considerations in Alaska (such as including wind and visibility components) can extend winter weather impact-based forecasting’s utility. Additionally, lessons learned from the process of adapting a national product to specific regional needs may inform best practices for gathering stakeholder input and feedback.

Charging infrastructure assessment for shared autonomous electric vehicles in 374 small and medium-sized urban areas: An agent-based simulation approach

This research examines the use of Shared Autonomous Electric Vehicles (SAEVs) in 374 U.S. small and medium-sized urban areas, focusing on fleet and infrastructure needs through agent-based simulations. It assesses metrics such as fleet size, trips per vehicle, and charging station requirements, considering two charger types: Level 2 and Level 3. The findings show significant spatial differences in SAEV operations and infrastructure across these cities. Statistical analysis links these variations to regional road networks and travel patterns. The study finds Level 3 chargers more efficient, requiring fewer stations and enabling more trips per vehicle compared to Level 2 chargers. Furthermore, Level 3 chargers exhibit a greater number of trips per SAEV and a higher ratio of vehicles to charging stations. These findings highlight the significance of considering charging infrastructure characteristics to optimize SAEV fleet performance and promote sustainable transportation systems in urban areas. This study significantly contributes by identifying the spatial variation and correlates of the SAEVs' operational and charging infrastructural performance. Policymakers, urban planners, and transportation service providers can leverage these insights to design and implement effective charging infrastructure for SAEV fleets, thereby advancing the transition to cleaner and more efficient mobility solutions.

Automatic Accident Avoidance System

Abstract: As much as road travel has improved lives for the better part it also has proposed many challenges. Along with taking lives of those if countless people it also has been an economic burden on the country, in India the cost of road travel accidents in 2019 was estimated to be 12 billion USD. Along with loss of lives, serious injuries, damage to public infrastructure and with huge financial burden on the tax payers it becomes a major in road block in the urbanization of developing countries, a lot of the steps have been taken by the government regarding this issue including awareness among the general public about using the safety features present in their respective automobile, avoiding driving under the influence of substances, regulating the manufacturing standards of automobiles so they can withstand the damage and keep the passengers safe. But these steps are primary based on the presumption on the reliable decision making of the human driver. A lot more could be achieved by devising methods for accident prevention that don’t rely on human intervention. In this project we have devised a system that works automatically using an ATmega328mp microcontroller paired with an ultrasonic distance sensor that can detect the obstacles facing the automobile and can make a decision to warn the driver and if ignored can override the control of the human driver.

Development of novel magnetorheological dampers with low-speed sensitivity for flying car suspensions

As urban traffic environments continue to grow in complexity, there is an urgent need for a versatile mode of transportation that seamlessly transitions between terrestrial and aerial mobility. In conventional magnetorheological damper (CMRD), the magnetorheological fluid flowing through the narrow annular gap between the piston and cylinder in CMRD results in a damping force directly proportional to velocity. As velocity increases, the damping force rises sharply, posing a significant risk to the vehicle’s mechanical structure and passenger safety. This velocity sensitivity restricts their applications primarily to standard commercial vehicle suspension systems. They face significant challenges when it comes to high-speed impact scenarios. To overcome this limitation, enhance the shock-absorbing capacity of flying cars, ensure passenger safety, and improve passenger comfort during the landing phase, this study introduces a novel magnetorheological damper (NMRD) with unique internal channel structure embedded in a circular permanent magnet. In road travel mode, NMRD maintains a wide dynamic range. During high-speed impact landing, when the impact force exceeds the threshold, the pressure relief channel opens, effectively reducing the peak impact force. This feature greatly expands the application range of magnetorheological dampers. The researches included simulations of the electromagnetic induction phenomenon within the piston, The pressure relief damping force inside the NMRD valve was accurately measured by using material testing system, the peak force and peak acceleration experienced by the two dampers during impact were tested using a dedicated drop hammer apparatus. These tests demonstrate that the NMRD exhibits superior impact resistance performance compared to CMRD. This highlights the promising potential for the NMRD’s application within the suspension systems of flying cars.

Systematic Research and Application of a 5G Medical Unmanned Aerial Vehicle to Deliver COVID-19 Nucleic Acid Samples.

The purpose of this study was to investigate the feasibility and effectiveness of COVID-19 throat swab samples delivered by medical drones in epidemic prevention and control. This study was carried out in both southern and northern hospital districts of the Affiliated Hospital of Jiangnan University from May to October 2022. The main participants were the Affiliated Hospital of Jiangnan University and Zhejiang Antwork Technology Co., Ltd. We first constructed an urban medical unmanned aerial vehicle (UAV) delivery system and developed a UAV-specific storage box for COVID-19 samples. The UAV system was used to transport COVID-19 throat swab samples from the northern hospital district to the southern hospital district, and the following indexes were obtained: (1) flight time of COVID-19 samples delivered by UAV, (2) real-time temperature of COVID-19 nucleic acid samples during transportation, and (3) the time of distribution of COVID-19 nucleic acid samples by road traffic as measured using the Baidu Maps application, compared with the flight time of UAV. The COVID-19 sample delivery system for urban medical UAV mainly consists of intelligent logistics UAV, low-temperature COVID-19 throat swab sample storage box, unmanned logistics hub, and cloud operation control platform. The flight distance between the northern and southern districts of the Affiliated Hospital of Jiangnan University was 10 km, and the ground distance was 24 km. From May 11 to October 28, 2022, a total of 1,190 UAV flights occurred. The average flight time was 13 minutes, which was 40 to 70 minutes faster than the average road travel time required for manual delivery of COVID-19 throat swab samples. At different time points in the day, UAV delivery efficiency increased by 67.5% to 82%. The use of 5G with the Internet of Things and UAV technology to deliver nucleic acid samples has the characteristics of fast speed, being unaffected by ground traffic conditions, and the ability to ensure the safety of nucleic acid samples in the transportation process, which is worthy of further study.

Study Roadmap Selection Based on the Thermal Comfort of Street Trees in Summer: A Case Study from a University Campus in China

The intensification of the urban heat island effect, characterized by persistent high temperatures in Chinese cities during summer, has led to notable shifts in urban residents’ activity patterns and travel preferences. Given that street trees, as fundamental components of urban road networks, have significant interaction with residents, it is imperative to investigate their thermal comfort impact. This study aims to enhance the comfortable summer travel experience for urban dwellers. Fujian Agriculture and Forestry University (FAFU) was selected as the case study site, with eight street tree species identified as measurement points. The summer solstice (21 June 2023) served as the representative weather condition. Through monitoring temperature and humidity, the study explored the correlation between street tree species, their characteristic factors, and thermal comfort. Utilizing ENVI-met and ArcGIS, the thermal comfort of campus travel routes was assessed, leading to the development of a summer travel guide based on thermal comfort considerations. The research novelty lies in applying a combined ENVI-met 5.0.2 and ArcGIS 10.8 software approach for modelling and visualizing the microclimate, which enables a more precise analysis of the thermal comfort variations of different campus paths, thus improving the accuracy and applicability of the results in urban planning. The findings reveal several points. (1) Different street trees possess varying capacities to enhance human comfort, with Falcataria falcata and Mangifera indica exhibiting the strongest cooling and humidifying effects, whereas Bauhinia purpurea and Amygdalus persica perform the poorest. Additionally, the research confirms ENVI-met’s scientific accuracy and practicality for microclimate studies. (2) The contribution of street trees to the comfort of campus road travel is primarily determined by the Sky View Factor (SVF), which negatively correlates with cooling and humidifying intensity and positively with thermal comfort. (3) During midday, travel comfort conditions on campus roads are better. Based on the thermal comfort assessment, a summer roadmap was created for the campus. In this case, the campus roads indicated by road A are considered the best travel routes in summer, and the roads indicated by roads B and C are considered alternatives for travelling. This practical application demonstrates how theoretical research results can be translated into practical tools for daily commuting and urban planning. It provides data references and empirical cases for the scientific optimization and enhancement of urban roads.

Pengaruh Aksesibilitas Jalan Ende – Nuabosi Terhadap Peningkatan Perekonomian Masyarakat Kecamatan Ende

Nuabosi is one of the villages in the Ende District area, where this village helps improve the economy of Ende District in terms of meeting economic needs, namely in the form of various agricultural and plantation products. However, even though Nuabosi Village is sufficient to meet the economic needs of Ende District, distance and inadequate infrastructure conditions are some of the factors that influence the level of accessibility in Ende District. This research aims to determine the value and level of accessibility in the villages of Ende District. The accessibility measuring parameter data used is travel time, road pavement conditions, supporting facilities, and travel rates or costs. Data analysis uses quantitative descriptive analysis and crosstab analysis methods, using the total accessibility calculation formula. The results of the analysis based on a review of accessibility parameters and crosstabs show that the highest accessibility value for each village is Ria Raja Village with a value of 2.18 and the lowest is Tina Bani Village with a value of 0.37. The average level of accessibility in Ende District as a whole is 0.83, indicating a high level of accessibility, where the existing road conditions can influence the improvement of the economy of the people in Ende District. Crosstab analysis also shows that most of the factors determining accessibility are related to the socio-economic conditions of the community.

Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic Information

Origin-destination (OD) flow contains population mobility information between every two regions in the city, which is of great value in urban planning and transportation management. Nevertheless, the collection of OD flow data is extremely difficult due to the hindrance of privacy issues and collection costs. Significant efforts have been made to generate OD flow based on urban regional features, e.g., demographics, land use, and so on, since spatial heterogeneity of urban function is the primary cause that drives people to move from one place to another. On the other hand, people travel through various routes between OD, which will have effects on urban traffic, e.g., road travel speed and time. These effects of OD flows reveal the fine-grained spatiotemporal patterns of population mobility. Few works have explored the effectiveness of incorporating urban traffic information into OD generation. To bridge this gap, we propose to generate real-world daily temporal OD flows enhanced by urban traffic information in this paper. Our model consists of two modules: Urban2OD and OD2Traffic . In the Urban2OD module, we devise a spatiotemporal graph neural network to model the complex dependencies between daily temporal OD flows and regional features. In the OD2Traffic module, we introduce an attention-based neural network to predict urban traffic based on OD flow from the Urban2OD module. Then, by utilizing gradient backpropagation, these two modules are able to enhance each other to generate high-quality OD flow data. Extensive experiments conducted on real-world datasets demonstrate the superiority of our proposed model over the state of the art.

An efficient safest route prediction-based route discovery mechanism for drivers using improved golden tortoise beetle optimizer

ABSTRACT Several commercialised route recommendation systems only consider the metrics like cost, time, and distance. The essential metric ‘safety’ is neglected by the existent systems. It suggests only the short way and doesn’t include any safety information, such as crime awareness, road availability. This paper describes an inventive ideology to discover the safest route with minimal risk score for security of the road travellers. Hence, a new safety route navigation mechanism is developed to solve the challenges in the traditional route discovery approaches using deep learning. In the developed route discovery mechanism, the examination of the safest roads is done by the developed deep learning network, where the network is trained with the inputs obtained from the roads, such as road surface conditions, Road users, weather conditions, traffic conditions, accidental cases, and crime areas. The availability of the safest route will be determined by the ‘Long Short-Term Memory with Attention Mechanism’ (LSTM-AM). The route discovery is done with the help of a developed ‘Fitness-based Golden Tortoise Beetle Optimizer’ (FGTBO) with multi-objective constraints like distance, time, and road availability. The implementation outcome of the developed route discovery scheme will be validated with the traditional route discovery approaches concerning various measures.

Examining inequalities in spatial access to national health insurance fund contracted facilities in Kenya.

Kenya aims to achieve universal health coverage (UHC) by 2030 and has selected the National Health Insurance Fund (NHIF) as the 'vehicle' to drive the UHC agenda. While there is some progress in moving the country towards UHC, the availability and accessibility to NHIF-contracted facilities may be a barrier to equitable access to care. We estimated the spatial access to NHIF-contracted facilities in Kenya to provide information to advance the UHC agenda in Kenya. We merged NHIF-contracted facility data to the geocoded inventory of health facilities in Kenya to assign facility geospatial locations. We combined this database with covariates data including road network, elevation, land use, and travel barriers. We estimated the proportion of the population living within 60- and 120-minute travel time to an NHIF-contracted facility at a 1-x1-kilometer spatial resolution nationally and at county levels using the WHO AccessMod tool. We included a total of 3,858 NHIF-contracted facilities. Nationally, 81.4% and 89.6% of the population lived within 60- and 120-minute travel time to an NHIF-contracted facility respectively. At the county level, the proportion of the population living within 1-hour of travel time to an NHIF-contracted facility ranged from as low as 28.1% in Wajir county to 100% in Nyamira and Kisii counties. Overall, only four counties (Kiambu, Kisii, Nairobi and Nyamira) had met the target of having 100% of their population living within 1-hour (60min) travel time to an NHIF-contracted facility. On average, it takes 209, 210 and 216min to travel to an NHIF-contracted facility, outpatient and inpatient facilities respectively. At the county level, travel time to an NHIF-contracted facility ranged from 10min in Vihiga County to 333min in Garissa. Our study offers evidence of the spatial access estimates to NHIF-contracted facilities in Kenya that can inform contracting decisions by the social health insurer, especially focussing on marginalised counties where more facilities need to be contracted. Besides, this evidence will be crucial as the country gears towards accelerating progress towards achieving UHC using social health insurance as the strategy to drive the UHC agenda in Kenya.

A unified dataset for the city-scale traffic assignment model in 20 U.S. cities

City-scale traffic data, such as traffic flow, speed, and density on every road segment, are the foundation of modern urban research. However, accessing such data on a city scale is challenging due to the limited number of sensors and privacy concerns. Consequently, most of the existing traffic datasets are typically limited to small, specific urban areas with incomplete data types, hindering the research in urban studies, such as transportation, environment, and energy fields. It still lacks a city-scale traffic dataset with comprehensive data types and satisfactory quality that can be publicly available across cities. To address this issue, we propose a unified approach for producing city-scale traffic data using the classic traffic assignment model in transportation studies. Specifically, the inputs of our approach are sourced from open public databases, including road networks, traffic demand, and travel time. Then the approach outputs comprehensive and validated citywide traffic data on the entire road network. In this study, we apply the proposed approach to 20 cities in the United States, achieving an average correlation coefficient of 0.79 in average travel time and an average relative error of 5.16% and 10.47% in average travel speed when compared with the real-world data.

Estimating On-Road Transportation Carbon Emissions from Open Data of Road Network and Origin-Destination Flow Data

Accounting for over 20% of the total carbon emissions, the precise estimation of on-road transportation carbon emissions is crucial for carbon emission monitoring and efficient mitigation policy formulation. However, existing estimation methods typically depend on hard-to-collect individual statistics of vehicle miles traveled to calculate emissions, thereby suffering from high data collection difficulty. To relieve this issue by utilizing the strong pattern recognition of artificial intelligence, we incorporate two sources of open data representative of the transportation demand and capacity factors, the origin-destination (OD) flow data and the road network data, to build a hierarchical heterogeneous graph learning method for on-road carbon emission estimation (HENCE). Specifically, a hierarchical graph consisting of the road network level, community level, and region level is constructed to model the multi-scale road network-based connectivity and travel connection between spatial areas. Heterogeneous graphs consisting of OD links and spatial links are further built at both the community level and region level to capture the intrinsic interactions between travel demand and road network accessibility. Extensive experiments on two large-scale real-world datasets demonstrate HENCE's effectiveness and superiority with R-squared exceeding 0.75 and outperforming baselines by 9.60% on average, validating its success in pioneering the use of artificial intelligence to empower carbon emission management and sustainability development. The implementation codes are available at this link: https://github.com/tsinghua-fib-lab/HENCE.