Evening Peak Hours Research Articles

Research on the Application of Smart City Super Brain in the Upgrading and Reconstruction of Expressway—Take Chengdu Old Chuan-Zang Road as an Example

This paper explores the smart city super brain traffic flow prediction method. Through monitoring and studying the traffic volume and average speed of related intersections in the morning and evening peak hour and one normal hour, a total of 7413 data are collected, of which 5930 are used as training sets and 1483 are used as test sets. Long short-term memory network (LSTM) method is used to predict the changes of traffic flow at important traffic nodes along old Chuan-Zang Road in Chengdu and the changes of vehicle speed at typical time of one day. The predicted results are highly coincident with the actual data, which proves the accuracy and effectiveness of the model. According to the predicted results, through the simulation analysis of the transformation scheme of important nodes, the traffic operation of the study section before and after the transformation is evaluated. The results show that the average delay of vehicles is reduced by 23.82% compared with that before the transformation, and the pollutant emission and fuel consumption are also significantly reduced. LSTM, with its unique advantages in processing sequence data and solving long-term nonlinear problems, provides algorithms and basis for urban road upgrading.

Spatial and temporal variability of urban cyclists’ exposure to PM2.5 in Medellín, Colombia

Fine Particulate Matter (PM2.5) has become a major issue in cities around the world as it adversely affects human health and the environment. This study aims to develop a deeper understanding of the impact of bicycle lanes designs on cyclist exposure to air pollution in a developing-country city. PM2.5 concentrations were measured along a predefined route with different bicycle lane designs in the city of Medellín, Colombia. The measurement campaign was made between October and December 2020 during peak and off-peak hours on weekdays, where a total of 29 bicycle trips were carried out. To obtain accurate measurements, we used a laser-based particle monitoring system. The study's findings reveal that the bicycle route section without dedicated bicycle lanes had the highest PM2.5 exposure and inhaled dose per kilometer traveled. Furthermore, the mean PM2.5 exposure for cyclists during the morning peak hours was higher (33.8 μg/m3) compared to the evening peak (16.1 μg/m3) and off-peak hours (11.1 μg/m3). The inhaled PM2.5 dose was three times higher during morning peak hours than during off-peak hours and twice as high during evening peak hours. These results show that segregated cycling lanes on the sidewalk can considerably lower PM2.5 exposure and inhaled doses for cyclists when compared to other lane designs, highlighting the significance of infrastructure development in supporting sustainable transportation and public health.

Study on Community Detection Method for Morning and Evening Peak Shared Bicycle Trips in Urban Areas: A Case Study of Six Districts in Beijing

Examining the clustering characteristics and fluctuations within urban areas during peak hours through the lens of bike-sharing is of utmost importance in the optimization of bike-sharing systems and urban transportation planning. This investigation adopts the principles of urban spatial interaction network construction and employs streets as the fundamental units of analysis to model bike-sharing activities during morning and evening peak hours within Beijing’s six central districts. Subsequent to this, a comprehensive analysis of the network’s structural attributes was carried out. A Walktrap method, rooted in modularity analysis, was introduced to discern and scrutinize the clustering patterns and characteristics of communities within the network across different temporal intervals. Empirical findings reveal a predominant usage pattern of shared bicycles for short-distance travel during both morning and evening peak hours. Notably, distinctive community structures manifest during these periods, characterized by two large communities and multiple smaller ones during the morning peak, while the evening peak showcases a single large community alongside several medium-sized and smaller ones. Moreover, the extended interaction radius points to an expanded geographic range of interactions among streets. These findings bear significant implications for the management of urban transportation, bike-sharing enterprises, and urban residents, proffering valuable insights for the optimization of bike-sharing schemes and transportation strategies. These research findings not only contribute to enhancing urban transportation planning and bike-sharing systems but also provide robust guidance for advancing more efficient and sustainable urban transportation solutions, thereby fostering the sustainable development of cities.

Dynamic quantification of on-road emissions in Hong Kong: Impact from traffic congestion and fleet composition variation

Policymakers need a good understanding of on-road air pollutant emission patterns to manage air quality effectively, but it can be challenging to dynamically capture emission patterns with high spatial and temporal resolutions in cities. In this study, we used traffic congestion index extracted from Google Map, traffic density model and local emission factors to develop a dynamic on-road emission inventory for Hong Kong. The pollutants include NOx, CO, NMVOC, CO2, PM2.5, PM10, and CH4. On weekdays, exhaust emissions increased by up to 50.0% compared with those on Sunday due to higher traffic congestion index. High emission rates were observed on highways during weekday evening peak hours and shifted to regional centers on Sunday evenings. Public light buses, goods vehicles, and taxis were the most significant contributors of NMVOC, NOx, and CO emissions, respectively. Control policy testing showed that replacing diesel-fueled buses with electric buses could significantly reduce NOx and PM exhaust emissions, whereas replacing liquefied petroleum gas fueled public light buses with electric buses could efficiently control NMVOC emissions. Eliminating private cars older than 15 years could effectively reduce CO emissions. This study provides a dynamic on-road emission inventory for Hong Kong with high temporal and spatial resolutions. The results can be extended to real-time air quality simulations and human exposure research in the future.

A Study on Bicycle-Sharing Dispatching Station Site Selection and Planning Based on Multivariate Data

Bicycle-sharing is experiencing rapidly as a low-carbon transport mode of travel, with the advantages of low cost and sustainability. Bicycle-sharing operators use electronic fence parking points to manage bicycle-sharing, but it is too time-consuming and impractical to manage them individually. Therefore, it is necessary to cluster the electronic fence parking points and implement regionalized management. This study uses a Mean-shift clustering algorithm to cluster the electronic fence parking points on Xiamen Island, construct a bicycle-sharing dispatching station, and divide the management area. Singular value decomposition is employed to analyze the travel demand patterns of bicycle-sharing and travel characteristics. In addition, we constructed regression models to explore the relationship between the urban built environment and bicycle-sharing trips during the morning and evening peak hours. The study results show that: (1) The 73 dispatching stations constructed cover 86.72% of the bicycle-sharing data, with a good clustering effect. (2) The travel demand for bicycle-sharing shows multiple patterns and different characteristics in different spatial and temporal distributions, which are influenced by land use. (3) There are spatial and temporal differences in the impact of the urban built environment on bicycle-sharing trips, especially residential and enterprise poi densities have opposite effects on shared bicycle-sharing during morning and evening peak hours. The research results of this paper can serve in the planning of bicycle-sharing dispatch stations and the differentiated management and dispatch of bicycle-sharing, which can reduce operating costs and promote the development of sustainable urban transport.

Evaluation of Traffic Noise Level in Residential Areas Along Jalan Kluang - Case Study in Sri Gading, Batu Pahat, Johor

Noise, recognized as an adverse factor affecting human health and overall well-being, poses a significant public health concern. This study focuses on evaluating the traffic noise levels in residential areas along Jalan Kluang. Measurements were conducted in four selected residential areas, namely Taman Sri Saga, Taman Cempaka Putih, Kampung Kluang, and Taman Bahagia Baru, during morning (7.30 am - 9.30 am) and evening (4.00 pm - 6.00 pm) peak hours on weekdays. The results indicated that certain locations exceeded the noise limit that set by the Department of Environment (DOE). Kampung Kluang exhibited LAeq values of 71.10 dBA and 71.48 dBA during the morning and evening peak hours, respectively, surpassing the DOE noise limit. Taman Cempaka Putih also recorded noise levels, with LAeq values of 69.40 dBA and 66.46 dBA during the morning and evening peak hours, respectively. In contrast, Taman Sri Saga and Taman Bahagia Baru demonstrated LAeq values still below the DOE limit during both peak hours. Taman Sri Saga recorded LAeq values of 55.63 dBA and 57.78 dBA, while Taman Bahagia Baru recorded LAeq values of 57.58 dBA and 59.65 dBA during the morning and evening peak hours, respectively. Therefore, it is crucial to address the excessive noise levels in the affected residential areas to mitigate the detrimental effects and ensure a healthier living environment for the affected residents in those areas.

Study on Tram Travel-Time Reliability Characteristics for Optimal Traffic Signal Control

This paper quantitatively investigates the effects of the number of intersections between adjacent stations [Formula: see text] and different periods on tram travel-time reliability to provide a decision basis for adopting optimal signal-control methods. Station spacing data, intersection information on the line, 10 day planned operation schedules, and actual operation data of Zhangjiang tram line 1 were collected. The tram travel-time distributions were analyzed under seven typical scenarios (i.e., N = 0, 1, 2, 3, off-peak hours, morning peak hours, and evening peak hours). The results showed that the lognormal distribution described the tram travel time better than did the normal and gamma distributions. It was demonstrated that scenarios [Formula: see text], [Formula: see text], and off-peak hours were of high reliability; morning peak hours and evening peak hours were of medium reliability; and scenarios [Formula: see text] and [Formula: see text] were of low reliability. The unreliability of travel time increased along with the increase of the mean and the fluctuation of travel time. An optimized signal-control strategy based on travel-time reliability was put forward and VISSIM simulation of an interval was conducted, verifying the effectiveness of the proposed strategy. The findings will help tram-management agencies to adopt suitable and flexible signal-control strategies according to the actual varying condition.

Impacts of the Feeder-Related Built Environment on Taxi-Metro Integrated Use in Lanzhou, China

It is very common that taxi is used as a feeder mode to/from a metro station especially in third-tier cities of China. But research on taxi-metro integrated use is rather limited. This paper investigates the relationship between feeder-related built environment and taxi-metro integrated use by using taxi trajectory data in Lanzhou, China. Firstly, regression models are developed to explore the transfer distance of access/egress trips during peak hours. Then, the catchment area is delineated for collecting feeder-related environment variables. Finally, several negative binomial regressions are employed to examine the relationships between feeder-related built environment and taxi-metro integrated use during peak hours on weekdays. The results reveal that (a) people prefer to use taxi as feeder mode for metro access during morning peak hours and for metro egress during evening peak hours; (b) the transfer distance of taxi-metro integrated use is about 3.8 km; (c) higher mixed land use generates more taxi-metro integrated use during evening peak hours. Higher proportion of residential land use attracts more taxi-metro integrated use for metro access during morning peak hours. Those findings will help transport planners to develop tailored land-use interventions to improve transit accessibility and promote the sustainable multimodal travel.

Bus priority lane in Bengaluru: A study on its effectiveness and driver stress

This paper studies the effectiveness of the bus priority lane (BPL) for public transport buses in the city of Bengaluru in India. We use the travel times on the BPL corridor as a measure of the effectiveness of the BPL. We find that there is a significant improvement in the travel times after the introduction of the BPL; for the worst 10% of the travel times, we find an improvement between 4% and 28%. Our methodology involves extracting trips on the BPL and computing the travel times for these trips from a time series of GPS information. Our methodology is scalable and can be used to compute the travel times between any two given points in other similar studies. We supplement our results with a novel test (called the D-test) for comparing the levels of stressful driving in the following scenarios: (a) morning peak hours (IST 07:00 h to 11:00 h) versus evening peak hours (IST 17:00 h to 21:00 h), and (b) northward trips versus southward trips on the BPL. We are able to infer that the drivers are generally more stressed during the morning peak hours and during the southward trips on the BPL. Partitioning the BPL into segments, we show that a majority of the segments exhibit similar effectiveness and driver stress trends as the full BPL stretch. We anticipate that corrective measures for the betterment of travel times and driver stress levels (e.g., introducing additional buses subject to vehicle re-balancing constraints, carefully planning the bus schedules to regulate bus traffic throughout the day, etc.) in some segments can lead to further improvements in travel times and reduction in driver stress levels.

Leveraging probe data to model speeding on urban limited access highway segments: Examining the impact of operational performance, roadway characteristics, and COVID-19 pandemic

Stay-at-home orders - imposed to prevent the spread of COVID-19 - drastically changed the way highways operate. Despite lower traffic volumes during these times, the rate of fatal and serious injury crashes increased significantly across the United States due to increased speeding on roads with less traffic congestion and lower levels of speed enforcement. This paper uses a mixed effect binomial regression model to investigate the impact of stay-at-home orders on odds of speeding on urban limited access highway segments in Maine and Connecticut. This paper also establishes a link between traffic density and the odds of speeding. For this purpose, hourly speed and volume probe data were collected on limited access highway segments for the U.S. states of Maine and Connecticut to estimate the traffic density. The traffic density was then combined with the roadway geometric characteristics, speed limit, as well as dummy variables denoting the time of the week, time of the day, COVID-19 phases (before, during and after stay-at-home order), and the interactions between them. Density, represented in the model as Level of Service, was found to be associated with the odds of speeding, with better levels of service such as A, or B (low density) resulting in the higher odds that drivers would speed. We also found that narrower shoulder width could result in lower odds of speeding. Furthermore, we found that during the stay-at-home order, the odds of speeding by more than 10, 15, and 20 mph increased respectively by 54%, 71% and 85% in Connecticut, and by 15%, 36%, and 65% in Maine during evening peak hours. Additionally, one year after the onset of the pandemic, during evening peak hours, the odds of speeding greater than 10, 15, and 20 mph were still 35%, 29%, and 19% greater in Connecticut and 35% 35% and 20% greater in Maine compared to before pandemic.

Travel Mode Choice Behavior for High-Speed Railway Stations Based on Multi-Source Data

The service quality of connecting travel mode at high-speed railway (HSR) stations plays an essential role in the travel chain. Most travel choice behavior studies are based on survey data and focus on special scenarios, such as going to work or school. Few studies have addressed travel mode choices for connecting to HSR stations. Based on multi-source data, this study aims to investigate the influences of travel time, cost, reliability, detour, and land use on connecting mode choice behaviors at HSR stations. Taking the Xi’anbei railway station as an example, this study analyzes the behavior characteristics of connecting modes including conventional buses, subway transit, and taxis, and builds a connecting travel mode choice behavior model for arrival/departure at HSR stations based on a generalized additive model (GAM). The results show that connecting travelers at HSR stations prefer to choose subways and taxis during the morning and evening peak hours. The perceived costs for arriving travelers are less sensitive than those for departing travelers. Arriving travelers are significantly more sensitive to reliability than departing travelers. Connecting travelers are less sensitive to cost and more sensitive to reliability than commuting travelers. The research results can provide suggestions and guidelines for connecting facility planning in HSR stations.

Discovering periodic frequent travel patterns of individual metro passengers considering different time granularities and station attributes

Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity measure. Although conventional algorithms for periodic frequent pattern detection have numerous applications, there is still little research on periodic frequent pattern detection of individual passengers in the metro. The travel behavior of individual passengers has complex spatio-temporal characteristics in the metro network, which may pose new challenges in discovering periodic frequent patterns of individual metro passengers and developing mining algorithms based on real-world smart card data. This study addresses these issues by proposing a novel pattern for metro passenger travel pattern called periodic frequent passenger traffic patterns with time granularities and station attributes (PFPTS). This discovered pattern can automatically capture the features of the temporal dimension (morning and evening peak hours, week) and the spatial dimension (entering and leaving stations). The corresponding complete mining algorithm with the PFPTS-tree structure has been developed. To evaluate the performance of PFPTS-tree, several experiments are conducted on one-year real-world smart card data collected by an automatic fare collection system in a certain large metro network. The results show that PFPTS-Tree is efficient and can discover numerous interesting periodic frequent patterns of metro passengers in the real-world dataset.

Multi-Scale Geographically Weighted Elasticity Regression Model to Explore the Elastic Effects of the Built Environment on Ride-Hailing Ridership

Understanding the relationship between the built environment and the ride-hailing ridership is crucial to the prediction of the demand for ride-hailing and the formulation of the strategy for upgrading the built environment. However, the existing studies on ride-hailing ignore the scale effect and zone effect of the modifiable area unit problem (MAUP), and show a lack of consideration for the elastic relationship with spatial heterogeneity between built environment variables and ride-hailing ridership. Taking Chengdu as an example, this paper selects 12 independent variables based on the “5Ds” (density, diversity, design, destination accessibility and distance to transit) of the built environment, the dependent variables are the density of ride-hailing pick-ups in the morning and evening peak hours, and 11 spatial units are proposed according to different scales and zoning methods for the aggregation of built environment variables and ride-hailing pick-ups. With the goal of global optimal goodness-of-fit, we determined the optimal spatial unit by using the log-linear Ordinary Least-Squares (OLS) model. A multi-scale geographically weighted elastic regression (MGWER) model is formulated to explore the relative effect of the built environment on the ride-hailing ridership and spatial heterogeneity. The average value of positive elastic local regression coefficient of different variables is used to measure the relative positive impact of built environment factors, and the absolute value of the average value of negative elastic local regression coefficient is used to measure the relative negative impact of built environment factors. The results show that: (1) The MGWER model under the community unit division has the best global goodness-of-fit. (2) Different built environment variables have different elastic impacts on the demand for ride-hailing. For the morning peak hours and evening peak hours, the top three built environment factors with positive impacts are ranked as follows: commercial POI density > average house price > population density, and distance to CBD has the highest negative impacts on pick-up ridership. (3) The different local elasticity coefficients of the built environment factors at different stations are discussed, which indicate the spatial heterogeneity of the ride-hailing ridership. The optimal community zoning method can provide a basis for the zoning and scheduling management of ride-hailing. The results of the built environment variables with greater impact are conducive to the formulation of targeted urban renewal strategies in the process of adjusting the ridership of ride-hailing.

Understanding spatial-temporal travel demand of private and shared e-bikes as a feeder mode of metro stations

Private e-bikes and shared e-bikes are gradually becoming the preferred modes of feeders for a vast number of metro passengers in China, opening up new potential for sustainable urban transportation development. This study proposes a method to identify feeder behaviors from private and shared e-bikes to the metro using mobile phone signal data and shared e-bike operation order data in Nanning, China. Then, the Light Gradient Boosting Machine models are constructed to reveal the influence of various factors on the feeder demand for two kinds of e-bikes in metro stations, to promote integrated travel of e-bikes to the metro. The results show that the demand for private e-bikes in metro stations is significantly higher than that of shared e-bikes, and the demand for the two types of feeder modes during the morning and evening peak hours on weekdays is greater than that in other periods. Secondly, the density of educational facilities has a positive effect on the demand for both feeder modes, and it has a greater impact on the demand for private e-bikes. Thirdly, the distance to the city center has a non-linear effect on the demand for private e-bikes. The farther away from the city center, the more feeders use private e-bikes to travel, and the fewer feeders use shared e-bikes. These findings can help planners better understand how various factors influence feeder demand.

Pollutant concentrations and exposure variability in four urban microenvironments of London

We compared various pollutant concentrations (PM1, PM2.5, PM10, PNC, BC) at four different urban microenvironments (MEs) in London (Indoor, IN; Traffic Intersection, TI; Park, PK; and Street Canyon, SC). The physico-chemical characteristics of particles were analysed, and the respiratory deposition doses (RDD) were estimated. Field measurements were conducted over a period of 121 days. The mean PM2.5 (PNC) concentrations were found to be 9.47 ± 7.05 (16366 ± 11815), 8.09 ± 4.57 (10951 ± 6445), 5.11 ± 2.96 (7717 ± 4576), 3.88 ± 3.06 (5672 ± 2934) μg m−3 (# cm−3) at TI, SC, PK and IN, respectively. PM2.5, PM10 and PNC exhibited a trend of TI > SC > PK > IN; higher concentrations for PM1 and BC were observed at IN than PK due to the emissions from printers, producing a trend of TI > SC > IN > PK. We observed 12%–30% higher fine PM concentrations at TI and SC sites during morning peak (07:00–09:30) than the evening peak hours (16:00–19:00); while IN showed a smaller variation in fine PM concentrations compared with outdoor TI, PK and SC sites owing to their prevalence in the IN for a longer time. Fine and ultrafine PM containing potentially toxic trace transition metals including Fe, Ti, Cr, Mn, Al and Mg were detected by high resolution electron microscopy at all sites. There was a similar relative abundance of different elements at the TI, IN and PK sites, which suggests a transport of PM between MEs. RDD for PM1 was highest (2.45 ± 2.27 μg h−1) at TI for females during running; PM2.5 and PM10 were highest at SC (11.23 ± 6.34 and 37.17 ± 20.82 μg h−1, respectively). The results show that the RDD variation between MEs does not follow the PM concentration trend. RDD at PK was found to be 39%–53% lower than TI and SC during running for all the PM fractions. Overall, the study findings show the air quality variation at different MEs and reveals the exposure inequalities around the city, which enable the management of personal exposure by selecting appropriate MEs for different activities.

Performance Evaluation and Improvement of Main Road Intersections in Al-Bayda City Using Sidra Software

Intersections are one of the most crucial parts of urban transportation systems since they actively contribute to developing strategies to reduce traffic congestion. Congestion is one of the major issues that intersections face as a result of the growth in traffic, which cause lower service levels and longer waiting time. The main goal of this study was to evaluate the current traffic flow and Level of Service (LoS) at four signalized intersections at peak hours in the city of Al-Bayda utilizing sidra software. In addition, comparing the performance of traffic flow during the morning and evening peak hours at the selected intersections. The results revealed that the level of service of the four intersections was very low and ranged between F and D, which indicated that the delay time was very high, therefore, an improvement to these intersections was necessary. After exploring various alternatives, the level of service increased to B and D levels, which make the delay time acceptable in these intersections. In the morning period, the average delay time decreased by 43.6, 44.3, 32.3, and 42% for intersections no.1-4 respectively. In the evening period, the average delay time decreased by 45.8, 69.5, 57.5, and 52% for intersections no.1-4 respectively.

Time-varying and non-linear associations between metro ridership and the built environment

The metro is the backbone of the transport system in many cities. Analyzing the built-environment correlates of metro ridership is crucial for transit-oriented development (TOD) planning and practice. Although numerous studies went along this line, they have rarely considered the non-linearity and temporal heterogeneity in the association of metro ridership with the built environment. After collecting transit smart card data, geo-data, and mobile phone signal data, this study adopts the random forest model to reveal the complex association of hourly metro ridership in November 2019 in Chengdu (China) with the built environment in three times of day (i.e., morning peak, noon off-peak, and evening peak hours). Notably, the contribution of several variables, such as the number of station entrances/overpasses and parking density, has rarely been considered in the literature. The results confirm the presence of non-linearity and temporal heterogeneity in the aforementioned association. Access to the city center and population density are strong predictors of metro ridership in the morning peak hour, whereas employment density, enterprise density, and road density are strong predictors in the evening peak hour. There are great differences in the correlates of metro ridership in different periods. Critical TOD planning parameters are also identified from the partial dependence plots obtained from random forest modeling.

Impact Assessment of Particulate Pollutants in Selected Traffic Congested Areas in Zaria Metropolis, Kaduna State, Nigeria

Abstract It has been noticed that the concentration of particle pollutants in major nodal points in Zaria metropolis has continued to grow as the traffic volume increases. The concentration of particulate matter and meteorological data was measured within the Zaria metropolis and was limited to major ways within the town which include Kwangila, PZ, Kasua Mata, Agoro and Kofar Doka. The concentrations of the Particulate Matter in different particle sizes (P0.3, P0.5.P1.0, P2.5 and P5.0) were measured using particle counter, at each sampling point for 3hrs during the morning peak hours (7:00am to 10.00am), for 3hrs during the afternoon off peak hours (11.00am to 2:00pm) and for 3hrs for the evening peak hours (4:00 to 7:00pm) after a comprehensive traffic volume of vehicles plying the sampling points had been done for one month. The average traffic volume for the peak period (morning) across the five sampling points ranged from 2701-3060 while that of the evening peak hours ranged from 2676-3651. The average traffic volume for the off peak (afternoon) ranged from 1777-2255 with kwangila recording the highest traffic volume for both peak and off peak periods. The impact of traffic density on the concentration of particulate matter measured showed that increase in traffic led to a corresponding increase in the concentration of particulate matter recorded for both peak and off- peak periods. The P2.5 was used as bases for comparison with the AQI standard for peak and off peak periods. For peak hour session, the air quality at Kwangila, Kasua Mata and Agoro was classified as unhealthy and hazardous while that of PZ and Kofar Doka was classified unhealthy to people with heart and lungs diseases. The concentration of PM obtained for off-peak period for all the sampling points was classified moderate and safe when compared with AQI standard. In this regard, there is a need to not only promote public transportation with larger capacity of passengers but to also improve the public transportation system. This will reduce greenhouse gas emissions by providing a convenient option to reduce unnecessary driving and by lowering emissions per passenger kilometer travelled. The use of non-motorized means of transportation would also help in curbing excessive release of pollutants into the atmosphere. Planting of Trees along major ways to trap and absorb pollutants is another option of reducing the amount of pollutants in the atmosphere. The use of older vehicles should be discouraged. The older a vehicle is the more the tendency to emit more pollutants.

NALISIS KINERJA KERUSAKAN JALAN (RIGID PAVEMENT) PADA RUAS JALAN JAKENAN WINONG KABUPATEN PATI

The Jakenan-Winong road is a regional link between the Jakenan area and the cities of Pati and Juwana. Due to the high mobilization of vehicles due to the repair of the Pati Rembang road on this road section, not a few damage to the concrete surface (rigid pavement) has occurred, one of the causes of which is the overload of large vehicles. From the known problems, this research was conducted to identify whether the damage that occurred could affect road performance on that road section. Data analysis used the 1997 Indonesian Road Capacity Manual (MKJI) method to determine traffic performance. From the research results, it can be seen the level of road performance on Jakenan Winong streat, Pati City during morning peak hours, noon peak hours, and evening peak hours, the level of service is in category B. From the research results it can also be seen that the damage that occurred on the Jakenan -Winong road section did not affect the level of road performance.

Unravel the spatio-temporal patterns and their nonlinear relationship with correlates of dockless shared bikes near metro stations

ABSTRACT The dockless bike-sharing system has rapidly expanded worldwide and has been widely used as an intermodal transport to connect with public transportation. However, higher flexibility may cause an imbalance between supply and demand during daily operation, especially around the metro stations. A stable and efficient rebalancing model requires spatio-temporal usage patterns as fundamental inputs. Therefore, understanding the spatio-temporal patterns and correlates is important for optimizing and rescheduling bike-sharing systems. This study proposed a dynamic time warping distance-based two-dimensional clustering method to quantify spatio-temporal patterns of dockless shared bikes in Wuhan and further applied the multiclass explainable boosting machine to explore the main related factors of these patterns. The results found six patterns on weekdays and four patterns on weekends. Three patterns show the imbalance of arrival and departure flow in the morning and evening peak hours, while these phenomena become less intensive on weekends. Road density, living service facility density and residential density are the top influencing factors on both weekdays and weekends, which means that the comprehensive impact of built-up environment attraction, facility suitability and riding demand leads to the different usage patterns. The nonlinear influence universally exists, and the probability of a certain pattern varies in different value ranges of variables. When the densities of living facilities and roads are moderate and the relationship between job and housing is relatively balanced, it can effectively promote the balanced usage of dockless shared bikes while maintaining high riding flow. The spatio-temporal patterns can identify the associated problems such as imbalance or lack of users, which could be mitigated by corresponding solutions. The relative importance and nonlinear effects help planners prioritize strategies and identify effective ranges on different patterns to promote the usage and efficiency of the bike-sharing system.