Road Links Research Articles

Kajian Aspek Keselamatan Jalan Terhadap Jalan Daerah Kabupaten Tana Toraja Untuk Mendukung Kegiatan Ekonomi Dan Pariwisata

Infrastructure development play important role in order to develope the tourism sector. Tana Toraja is a potential region to be developed on tourism sector. One of the problem is insufficient of road infrastructure for both connectivity and accesbility issues. This study aimed to give recommendations of road safety as to be technical consideration for Directorate General Highways to budgeting State Budget (APBN) in regional road in tana Toraja. This study was conducted on 5 link of regional roads that were considered as the potential to support the tourism sector. Qualitative approach is used as methodology trough data collection and information for analysis. The results show that all of the road link observed has geometric problems (i.e substandard road) and potential to cause accidents, such as road grade more than 10%, width of lane less than 3 meters, inadequate road shoulders, small radius of curve, and hazard objects on the side of the road side. Recommendations to fullfillment road standards must be applied for both the design stage and construction stage.

Estimation Method for Road Link Travel Time Considering the Heterogeneity of Driving Styles

To solve the problem of low automatic number plate recognition (ANPR) data integrity and low completion accuracy of incomplete traffic data, which affects the quality and utilization of ANPR data, this paper proposed a model for estimating the travel time of the road link that considers the heterogeneity of the driving styles. The travel time of historical road sections in the road network was extracted from ANPR data. The driving crowd was clustered through density-based spatial clustering of applications with noise (DBSCAN) based on the time slot, the number of trips, and the travel time. To avoid the excessive data difference between different classes and the distortion of the complement data, the Lagrange interpolation method was adopted to complement the missing road link travel time within each cluster. Taking Ningbo city in China as an example, the travel time completion accuracies of the proposed method and the direct interpolation method were compared. The results show that the interpolation method considering the heterogeneity of driving styles is more sufficient to increase the completion accuracy by 37.4% compared with the direct interpolation manner. The comparison result verifies the effectiveness of the proposed method and can provide more reliable data support for the construction of the transportation system.

Assessing the risk posed by flash floods to the transportation network in southwestern China

Flash flood can cause dramatic damages to the infrastructure of transportation networks. Because of the uncertainty of flash floods and the access function of roads, assessing the impact of flash floods on transportation systems has been a great challenge. In this study, we proposed a framework for evaluating the risk posed by flash floods to transportation network, which considers the interruption probability of road links and the expected impact on the access function of transportation. To assess the probability of interruption, we used the random forest model to output the flash floods susceptibility. Then, a multiple centrality model was used to evaluate the expected impact when a link was interrupted. The risk takes into account the flash floods susceptibility and road multiple centrality, and it identify critical links. Using the natural break points method, the result of flash floods susceptibility, road multiple centrality and risk were divided into five levels: extremely low, low, moderate, high and extremely high. Approximately 30.8% of the roads were found to be at high and extremely high risk, and most of these roads were located in Sichuan and Yunnan provinces. The flash floods susceptibility contributes more to the risk than the road multiple centrality, thus, further study should be focused on the areas with high flash floods susceptibility. We discussed different disaster prevention and mitigation measures for the different types of high risk links. The results can improve our understanding of the risks posed by flash floods to road networks. The proposed generalized network risk analysis method provides a new perspective and tool for analysing the impact of flash floods on road network.

Simulation of PM2.5 Concentrations around the Proposed Yangon Outer Ring Road (Eastern Section) in Myanmar Using CALINE 4 Model

An increase in traffic volume has resulted in the deterioration of environmental quality and human health in Yangon as well as in the surrounding areas that are connected to the city via several road links. The Yangon Outer Ring Road Construction (YORR) (Eastern Section) is a priority project for solving traffic-related problems. This study aimed to simulate the current levels of PM2.5 concentration around the proposed YORR (Eastern Section) area using the CALINE 4 model and to evaluate the model’s performance. Air quality measurements of PM2.5 were carried out in five townships around the proposed road construction area-for one week at each monitoring location-from January 24th to March 2nd, 2021 using the Haz-Scanner Environmental Perimeter Air Station. When compared to the ambient air quality guidelines of Myanmar, the International Finance Corporation, and the World Health Organization, the observed PM2.5 concentrations were found to be usually high at all locations, except in Kyauktan township. Statistical analysis indicated that the CALINE 4 model performed satisfactorily with a coefficient of determination of 0.85-0.90, fractional bias of 0.03-0.50, and normalized mean square error of 0.001-0.100. It is crucial that mitigation measures, including policies regarding the use of low PM emission vehicles and road-side barriers, be implemented by regulatory authorities during and after the YORR construction.

Deep Fusion for Travel Time Estimation Based on Road Network Topology

With the wide application of vehicular location-based services, precise estimation of the travel time plays a crucial role in intelligent transportation systems, such as driving navigation, traffic monitoring, and route planning. Recent methods have made significant progress on public datasets, but are not satisfied for current ride-hailing platforms with complex road network topology and dynamic traffic fluctuation. In this article, we propose an end-to-end Deep Fusion framework for Travel Time Estimation, which exploits multisource heterogeneous traffic information within an encoder–decoder architecture. Specifically, we explore a relational fusion network to learn the relationship of road link segments, and employ an attention mechanism to capture efficient correlations among spatial and temporal features. Extensive experiments have been conducted on two large-scale real-world traffic datasets collected by DiDi Corporation (DiDi) platform, and the results have demonstrated our effectiveness compared with the state of the art.

Operational Data-Driven Intelligent Modelling and Visualization System for Real-World, On-Road Vehicle Emissions—A Case Study in Hangzhou City, China

On-road vehicle emissions play a crucial role in affecting air quality and human exposure, particularly in megacities. In the absence of comprehensive traffic monitoring networks with the general lack of intelligent transportation systems (ITSs) and big-data-driven, high-performance-computing (HPC) platforms, it remains challenging to constrain on-road vehicle emissions and capture their hotspots. Here, we established an intelligent modelling and visualization system driven by ITS traffic data for real-world, on-road vehicle emissions. Based on the HPC platform (named “City Brain”) and an agile Web Geographic Information System (WebGISs), this system can map real-time (hourly), hyperfine (10~1000 m) vehicle emissions (e.g., PM2.5, NOx, CO, and HC) and associated traffic states (e.g., vehicle-specific categories and traffic fluxes) over the Xiaoshan District in Hangzhou. Our results show sharp variations in on-road vehicle emissions on small scales, which even fluctuated up to 31.2 times within adjacent road links. Frequent and widespread emission hotspots were also exposed. Over custom spatiotemporal scopes, we virtually investigated and visualized the impacts of traffic control policies on the traffic states and on-road vehicle emissions. Such results have important implications for how traffic control policies should be optimized. Integrating this system with chemical transport models and air quality measurements would bridge the technical gap between air pollutant emissions, concentrations, and human exposure.

Modeling Road Link-Level Truck Travel Times Using On- and Off-Network Characteristics

Travel times of freight trucks (or trucks) play a major role in trip planning, identification of efficient routes, and allocating resources for implementation of strategies. While research on travel time estimation models for passenger cars or traffic stream is documented in the literature, their applicability for trucks remains debatable. Truck travel is influenced by the road characteristics, surrounding land use, and demographics of an area. The focus of this research, therefore, is on estimating the truck travel times using the on-network (road) and off-network (land use and demographics) characteristics. Truck travel time data for 501 road links in North Carolina from 2019 were processed for four times of the day and two days of the week. Generalized estimating equations (GEE) were used with the average truck travel time per mile (ATTTPM) as the dependent variable. Spatial proximity (buffers of 0.25 mi, 0.50 mi, and 1 mi) and spatial weights (distance decay functions like 1/d, 1/d2, and 1/d3) were explored to check the best possible approach for capturing off-network characteristics. The office (business park and administrative areas), transportation (rest areas and parking facilities), heavy commercial, and light industrial land uses have an increasing influence on the ATTTPM. The linear model developed using data from a 0.25-mi buffer is best suited to estimate the ATTTPM. Off-peak hour delivery incentives or truck priority systems on road links near these land uses can be implemented to improve truck mobility. The methodology illustrated in this research is transferable and could be used for estimating truck travel times across a region.

Study on the Extraction Method of Sub-Network for Optimal Operation of Connected and Automated Vehicle-Based Mobility Service and Its Implication

There have been enormous efforts to implement automated vehicle-based mobility (AVM) by considering smart infrastructure such as cooperative intelligent transportation system. However, there is lack of consideration on economical approach for an optimal deployment strategy of the AVM service and smart infrastructure. Furthermore, the influence of travel demand in service area has been ignored. We develop a new framework for maximizing the profit of connected and automated vehicle-based mobility (CAV-M) service using cost modeling and metaheuristic optimization algorithm. The proposed framework extracts an optimal sub-network, which is selected by a set of optimal links in the service area, and identifies an optimal construction strategy for the smart infrastructure depending on given operational design domain and travel demand. Based on service network analyses with varying demand patterns and volumes, we observe that the optimal sub-network varies with the combination of trip demand patterns and volumes. It is also found that the benefit of deploying the smart infrastructure is obtainable only when there are sufficient travel demands. Furthermore, the optimal sub-network is always superior to raw network in terms of economical profit, which suggests the proposed framework has great potential to prioritize road links in the target area for the CAV-M service.

Predicting pedestrian crash locations in urban India: An integrated GIS-based spatiotemporal HSID technique

Pedestrians are one of the most vulnerable road users globally. Recent years have witnessed an increasing interest among the scientific community to analyze and enhance pedestrians' safety in an environment dominated by motor vehicles. This study proposes a three-step methodology to identify current and future critical pedestrian crash hotspots. Firstly, available multi-year crash data from two cities in India is digitized, and the spatial autocorrelation tool is used to determine the pedestrian crash hotspots. Secondly, space-time cube and emerging hotspot analysis are carried out to predict crash hotspots along urban streets. Finally, Hotspot Identification (HSID) methods, i.e., Equivalent Property Damage Only (EPDO) and Upper-tail Critical Tests are used to rank the road links based on spatio-temporal crash severity leading to the identification of links needing urgent interventions. The proposed three-step integrated methodology is novel and has never been used to simultaneously identify and prioritize the critical pedestrian crash locations as it has been done in the present study. The developed methodology identifies sections of arterial roads—Strand Road and AJC Bose Road in Kolkata and Gota Road in Ahmedabad, as the critical hotspot links that require urgent intervention.

High-resolution mapping of regional traffic emissions using land-use machine learning models

Abstract. On-road vehicle emissions are a major contributor to significant atmospheric pollution in populous metropolitan areas. We developed an hourly link-level emissions inventory of vehicular pollutants using two land-use machine learning methods based on road traffic monitoring datasets in the Beijing–Tianjin–Hebei (BTH) region. The results indicate that a land-use random forest (LURF) model is more capable of predicting traffic profiles than other machine learning models on most occasions in this study. The inventories under three different traffic scenarios depict a significant temporal and spatial variability in vehicle emissions. NOx, fine particulate matter (PM2.5), and black carbon (BC) emissions from heavy-duty trucks (HDTs) generally have a higher emission intensity on the highways connecting to regional ports. The model found a general reduction in light-duty passenger vehicles when traffic restrictions were implemented but a much more spatially heterogeneous impact on HDTs, with some road links experiencing up to 40 % increases in the HDT traffic volume. This study demonstrates the power of machine learning approaches to generate data-driven and high-resolution emission inventories, thereby providing a platform to realize the near-real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.

Spatio-temporal variability of vehicular traffic, noise and temperature of Minna City, Niger State, Nigeria

This study investigated spatio-temporal variability of vehicular traffic noise and temperature of Minna city, Niger State. The study used the 15 minutes Septa-Square technique to count the number of vehicles across the purposively selected junctions and round-abouts. The Noise Meter Level (Dosemeter) was used to generate the vehicular traffic noise at the selected junctions and roundabouts. The thermometer was used to capture the hotness of the selected roundabouts and junctions. The result of the study showed that Tunga Roundabout had 7032 vehicular counts, which was the highest vehicular traffic flow in the city. Barago Roundabout had 93.4dB recorded as the highest noise level and Tunga Roundabout recorded 360C as the highest temperature reading. The afternoon and evening had the highest records of vehicular traffic flow, increased traffic noise level and the temperature readings. The study recommended the construction of flyover to reduce traffic flow especially at Tunga and City Gate roundabouts. It recommended the construction of link roads to divert traffics to other parts of the city, plant trees at the roundabouts and junctions in order to cushion the effects of high temperature without further delay.

Estimating Truck Travel Time to Passenger Car or Traffic Stream Travel Time Ratio in North Carolina, USA

ABSTRACT The differences in travel times of passenger cars, traffic stream, and trucks depend on the area type, temporal factors, reference speed, and traffic condition. These explanatory variables account for the effect of geometric conditions and variations in the traffic flow. The focus of this research is to examine the correlations and estimate truck travel time to passenger car or traffic stream travel time ratio of a road link (dependent variable) as a function of these explanatory variables. Travel time data for Mecklenburg County and Iredell County in North Carolina, USA were gathered for the year 2017 to examine correlations, develop generalized estimating equations (GEE) models, and identify explanatory variables influencing the ratios. Gamma log-link distribution-based models are the best-fitted models to estimate the average travel time (ATT) of trucks to the ATT of passenger cars or traffic stream ratios. Notable differences in the coefficients were observed when the ATT of trucks was compared with the ATT of passenger cars or traffic stream. The area type (urban or rural) was observed to influence the ratios differently. The influence of traffic condition, reference speed (or free-flow speed), day-of-the-week (DOW) and time-of-the-day (TOD) on the ratios also varied with the area type.

Multivariate Correlation-aware Spatio-temporal Graph Convolutional Networks for Multi-scale Traffic Prediction

Traffic flow prediction based on vehicle trajectories collected from the installed GPS devices is critically important to Intelligent Transportation Systems (ITS). One limitation of existing traffic prediction models is that they mostly focus on predicting road-segment level traffic conditions, which can be considered as a fine-grained prediction. In many scenarios, however, a coarse-grained prediction, such as predicting the traffic flows among different urban areas covering multiple road links, is also required to help government have a better understanding on traffic conditions from the macroscopic point of view. This is especially useful in the applications of urban planning and public transportation planning. Another limitation is that the correlations among different types of traffic-related features are largely ignored. For example, the traffic flow and traffic speed are usually negatively correlated. Existing works regard these traffic-related features as independent features without considering their correlations. In this article, we for the first time study the novel problem of multivariate correlation-aware multi-scale traffic flow predicting, and we propose a feature correlation-aware spatio-temporal graph convolutional networks named MC-STGCN to effectively address it. Specifically, given a road graph, we first construct a coarse-grained road graph based on both the topology closeness and the traffic flow similarity among the nodes (road links). Then a cross-scale spatial-temporal feature learning and fusion technique is proposed for dealing with both the fine- and coarse-grained traffic data. In the spatial domain, a cross-scale GCN is proposed to learn the multi-scale spatial features jointly and fuse them together. In the temporal domain, a cross-scale temporal network that is composed of a hierarchical attention is designed for effectively capturing intra- and inter-scale temporal correlations. To effectively capture the feature correlations, a feature correlation learning component is also designed. Finally, a structural constraint is introduced to make the predictions on the two scale traffic data consistent. We conduct extensive evaluations over two real traffic datasets, and the results demonstrate the superior performance of the proposal on both fine- and coarse-grained traffic predictions.

Identification of road links with the gravest network impacts when blocked concurrently

The identification of a combination of links which can cause the gravest impact on network performance, when interrupted simultaneously, is of great practical importance. The links can be spatially dispersed due to an event such as an earthquake or due to a combination of accidents and disasters. This task is, thus, extremely computationally demanding when applied to real-world networks. We focused on approaches which are both capable of accomplishing this task and where the computational time is acceptable for application in practice. We tested three algorithms based on known heuristic methods: Simulated Annealing (SA), Guided Local Search (GLS) and Variable Neighborhood Search (VNS). The algorithms were modified in the sense of adjusting the searching neighborhood. All the algorithms were subsequently applied to four actual road networks in order to evaluate the impacts of complete simultaneous blockage of four and ten links. The results suggest that the modified SA algorithm identified scenarios with worse consequences than the algorithms based on GLS and VNS. The SA results, for the setting with four interrupted links, were even comparable with those obtained from a deterministic algorithm (which evaluates the entire state space). The algorithm based on SA was also performing best for situations with ten concurrently blocked links. The approach based on SA is thus suitable when modeling the potential impacts of events where a large number of concurrently blocked links is expected. Network managers will thus be able to monitor the immediate state of the network and potential risks related to network disintegration.

Prediction of travel time on urban road links with and without point detectors

This paper proposes a short-term rolling horizon framework for the within-day prediction of travel times on links with and without point detectors (referred to as observed and unobserved links respectively) along a selected path covered in the Hong Kong journey time indication system (JTIS). In Hong Kong JTIS, the number of point detectors on major roads is usually limited due to the financial budget and site constraints in the densely populated urban area. However, the prediction of the travel times on urban road corridors particularly on the links without point detectors is also valuable to road users and traffic authorities. This paper proposes a 2-stage framework based on functional principal component analysis and maximum likelihood estimation method to predict the mean and standard deviation of the travel times on the study path and observed links as well as unobserved links once every 2 min for the next 30 min. An urban road network in Hong Kong is selected as a case study. The prediction results are validated using an independent dataset from JTIS, demonstrating the practical applicability of the proposed framework.

Determinants of Occupational Health and Safety Implementation in Road Construction Projects in Kenya: A Case of Construction of Upper Hill – Mbagathi Link Road

This study was purposed to find out the level of administration of Occupational Health and Safety which affects the construction worker in road construction projects. This was achieved by examining the determinants of occupational health and safety implementation in the construction sector of the Kenyan economy. The study will benefit all the stakeholders in the construction industry and make them appreciate more about employee welfare. It is meant to be good material for consumption by anybody else interested in the goings-on in the construction industry in Kenya. The survey was limited to an ongoing road construction project i.e. Construction of Upper Hill – Mbagathi Link Road. A questionnaire was adopted and used to gather data in the research and was delivered to respondents by hand. The respondents were selected by use of random and stratified sampling methods for accountability of the information gathered. The response rate was 83% with 72% of the respondents agreeing that raised levels of awareness of occupational health and safety impact on the implementation of OHS issues in road construction projects. Seventy five percent of the respondents agreed that project financial resources greatly impact the implementation of OHS issues in construction projects. The study recommended the need to increase factoring financial resource allocations for OHS in construction projects to enable road contractors to carry out awareness sensitization workshops, ensure regular supply of PPEs and insurance to construction workers.

Micromobility implementation challenges and opportunities: Analysis of e-scooter parking and high-use corridors

To enhance the effectiveness of dockless shared micromobility vehicle services, address challenges created by these services, and achieve better implementation performance, cities need efficacious strategies to tackle two major challenges: parking demand and infrastructure improvements. This paper offers two techniques for analyzing areas of high parking demand and roadway segments of high micromobility vehicle demand. The proposed processes support efforts to assign free-floating parking and reduce vehicle clutter across cities, and assist transportation planners in identifying locations for road infrastructure improvement and ordinance enforcement. An unsupervised learning approach using vendor launching site analysis is used to appropriately locate dockless vehicle parking zones. Furthermore, this paper compares three methods for identifying high-demand corridors. Two shortest path models are generated to predict trip paths for trip data without trajectories, considering rider route preferences and infrastructure types. A Simplified Matching Heuristic (SMH) that uses trip data with trajectories, is developed herein to match trip trajectory data to the network street links. These analytical processes are applied in a case study of Dallas, TX. Results show that methods using shortest path models may poorly predict the actual path taken by e-scooter users. A discussion of policy implications for micromobility planning and management and potential environmental impacts of emerging transportation technologies are also presented.

ANALYSIS OF LANDSLIDES CAUSES ON BTS. KOTA BATU - BTS. KAB. KEDIRI ROAD

Landslides can occur due to decreased slope stability due to soil degradation and rock degradation making up the slopes. The problem of land mass movement occurred in the Bts. Kota Batu - Bts. Kab. Kediri Road (Link 187) Ngroto Village, Pujon District, Malang Regency, precisely at KM Mlg 31+350. This study is to determine the cause of the landslide on the Bts. Kota Batu - Bts. Kab. Kediri (Link 187) KM Mlg 31+350 and to find out the appropriate handling method to overcome the landslide. The factors causing landslides were analyzed from various sides as follows: due to existing conditions, due to changes in land use, topographic conditions, and geological conditions. The selection of the handling method, according to the situation and conditions in the field, is the use of a masonry retaining wall structure, the application of sheet piles, the handling of groundwater around the landslide area, and the normalization of rivers.

CORRELATION BETWEEN SERUM LEVELS OF FOLLICLE STIMULATING HORMONE AND GRAAFIAN FOLLICLE VASCULARITY ON DOPPLER IN INFERTILE FEMALES OF LAHORE

Objective: To determine correlation between serum levels of Follicle Stimulating Hormone and Graafian Follicle vascularity on Doppler ultrasound in infertile females of Lahore, Pakistan.
 Study Design: Cross sectional study.
 Place and Duration of Study: Prime Ultrasound center, 2-Shalamrar Link Road, Mughalpura, Lahore, Pakistan From Mar 2018 to Sep 2019.
 Methodology: A total of 100 infertile females using simple random sampling were taken. All the married women of reproductive age and married for >12 months who have not conceived despite regular unprotected intercourse, having no other uterine and endometrial or ovarian pathology were taken. The patients who fail to provide serum hormone laboratory reports were excluded. Transvaginal ultrasonography was done for all females and graafian follicle vascularity on Doppler was measured on day 12 of the cycle.
 Results: The Median age of females was 28 (30-25) years range from 19 and 46 years. The median Follicle Stimulating Hormone levels at day 12 was 6 (8.25-5.22) mlU/ml with minimum and maximum FSH values as 3.20 and 40 mlU/ml. The median follicle size was 13.65 (16.04-10.84) (mm) with minimum and maximum size as 10 mm and 26 mm. There were 61% females who had Grade–1 perifollicular vascularity, 24% females had grade-2 and 15% females had grade-3 of perifollicular vascularity. There was significant positive correlation between perifollicular vascularity grades and Follicle-stimulating hormone levels day 12 (mm) i.e., r=0.309, p-value <0.001.
 Conclusion: It is concluded that perifollicular vascularity was found to correlate with serum Follicle-stimulating hormone levels, so graafian follicle vascularity on.......

Improving the Performance of Transportation Networks: A Semi-Centralized Pricing Approach

Improving the performance of transportation network is a crucial task in traffic management. In this paper, we start with a cooperative routing problem, which aims to minimize the chance of road network breakdown. To address this problem, we propose a subgradient method, which can be naturally implemented as a semi-centralized pricing approach. Particularly, each road link adopts the pricing scheme to calculate and adjust the local toll regularly, while the vehicles update their routes to minimize the toll costs by exploiting the global toll information. To prevent the potential oscillation brought by the subgradient method, we introduce a heavy-ball method to further improve the performance of the pricing approach. We then test both the basic and improved pricing approaches in a real road network, and simultaneously compare them with several baselines. The experimental results demonstrate that, our approaches significantly outperform others, by comprehensively evaluating them in terms of various metrics including average travel time and travel distance, winners and losers, potential congestion occurrence, last arrival time, toll costs and average traffic flows, with two different O-D profiles.