Hotspot identification considering daily variability of traffic flow and crash record: A case study

Traffic Modeling and Safety Analysis on Motorway Ramps

Base saturation flow is a critical constraint used for evaluating the capacity of signalized intersections. The Indonesian Highway Capacity Manual (IHCM, 2023) is frequently used for signalized intersection planning in Indonesia. In heterogeneous traffic environments, the driver characteristics and behaviors exhibit significant variations. However, the base saturation flow was also influenced by traffic volume during the observation period. This study modeled the base saturation flow by analyzing differences in morning and afternoon peak hour data. The objective was to examine the base saturation flow at pretimed signal in Banda Aceh, with a focus on traffic volume fluctuations during peak hours. Four intersections were investigated in this study. Unmanned aerial vehicles were employed to record discharge flow pattern within targeted signalized intersection. Manual timing was conducted using stopwatches and geometric road data were collected through direct field measurements. Bayesian linear regression with a Gibbs sampling approach was utilized to recalibrate the saturation flow rate coefficients. The base saturation flow during the morning peak is 284 We, while in the afternoon peak, it is 285 We, with We representing the effective intersection approach.

Non-pharmaceutical interventions and urban vehicle mobility in Seoul during the COVID-19 pandemic

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.

Identification of crash hot spots is the critical component of the highway safety management process. Errors in hot spot identification (HSID) may result in the inefficient use of resources for safety improvements. One HSID method that is based on the empirical Bayesian (EB) method has been widely used as an effective approach for identifying crash-prone sites. For the EB method, the negative binomial (NB) model is usually needed to obtain the EB estimates. Recently, some studies have shown that the Sichel (SI) model can be easily used in the EB modeling framework and potentially yield better EB estimates. The objective of this study was to compare the performance of the two crash prediction (SI and NB) models in identifying hot spots with the EB method. To accomplish the objective of this study, empirical crash data collected at highway segments in Texas were used to generate simulated crash counts. Three commonly used HSID methods (simple ranking, confidence interval, and EB) were applied with the use of simulated data. False positives, false negatives, and false identifications were calculated and compared across the methods. The simulation results in this study suggested that the SI-based EB method could consistently provide a better HSID result than the NB-based EB method. Moreover, EB methods yielded the lowest error percentage of the three HSID methods. This study confirmed that the EB technique was an effective method for identifying hazardous sites. On the basis of the findings in this study, it is recommended that transportation safety researchers consider the SI model as an alternative crash prediction model when the EB approach is used.

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

Public preference and acceptability of the travel reservation strategy in Xi'an, China

Private vehicle ownership does not pay attention to the capacity of the road that will cause congestion of the road network in urban areas. Setting The Tohpati Junction with Traffic Signaling Tool is not able to cope, the solution to this condition is to build an underpass. The purpose of the study is to analyze the performance of existing intersections and underpasses in 2025, analyze the amount of costs required for the underpass, analyze the direct benefits of underpass construction, and can be known whether the project is feasible or not with an economic feasibility analysis of the underpass investment built. In addition to using primary data and secondary data in calculations, the method of economic feasibility analysis as a comparison of costs and benefits with indicators Net Present Value (NPV), Benefit Cost Ratio (BCR) and Internal Rate of Return (IRR). The results showed the performance of existing intersections obtained a delay of 373 seconds /smp morning peak hours, for the peak hour of noon 364 seconds / smp, and the peak afternoon 364 seconds / smp, including the level of service F. Simpang assumption underpass built the value of delay peak hours morning 19.02 seconds / smp, for the peak hours of noon 19.33 seconds / smp, and the peak afternoon 18.32seconds / smp, including the level of service A. In realizing the construction of the underpass, it is necessary to pay Rp. 224,964,665,195.28 and direct benefit from the construction of the underpass at Simpang Tohpati in Denpasar City for the cost savings of road users.amounting to Rp.10.407.740.934,00. In general, the underpass construction project at Simpang Tohpati in Denpasar city is economically viable and the terms of the three bank interest rates of 12%,15% and 18% in one year result in a non-negative NPV value, BCR is not less than one and the IRR is not below the bank's interest rate, hence the analysis related to the sensitivity of the bank to the condition of I, II and III.

This work aims to minimize average delay for signalized traffic network along Jalan UMS under oversaturated condition using decentralized genetic algorithm (DGA). Relieving traffic network is a key challenge for a nation for improving its socio-economic systems and welfare to society. Control the traffic signal timing becomes a cost effective solution to reduce congestion since the space constraint limits the authority to build more infrastructures in city. Regrettably, most of the current traffic signal control systems are not yet optimized. They are preset based on historical traffic data; thus they are inadequate to cope with the dynamic changes in real traffic scenario. Therefore, this work proposes DGA to optimize the traffic network signal for reducing average delay during the morning peak hour. A comprehensive traffic model based on Public Works Department, Malaysia has been developed as the platform. The average delay experienced by vehicles to traverse the crossed intersection during morning peak hour is used as the performance metric to evaluate performances of the proposed algorithm. Simulation results show developed DGA is able to reduce the average network delay by optimizing the traffic signal.

Traditional or standard work schedules refer to the requirement that workers must be at work the same days and during the same hours each day. This requirement constrains work-related trip arrivals, and generates morning and afternoon peak hours due to the concentration of work days and/or work hours. Alternative work schedules seek to reschedule work activities away from this traditional requirement. The aim is to flatten the peak hours by spreading the demand (i.e., assigning it to the shoulders of the peak hour), lowering the peak demand. This not only would reduce societal costs but also can help to minimize the physical requirements. In this article, a two-stage optimization model is presented to quantify the effects of staggered work hours under incentive policies. In the first stage, a variation of the generalized quadratic assignment problem is used to represent the firm’s assignment of workers to different work starting times. This is the input of a nonlinear complementarity problem that captures the behavior of the users of the transportation network who are seeking to overcome the constraints imposed by working schedules (arrival times). Two examples are provided to show how the model can be used to (a) quantify the effects and response of the firm to external incentives and (b) evaluate what type of arrangements in starting times are to be made in order to achieve a social optimum.

In this paper, we take the example of Changchun City automobile trade logistics park. Based on the design parameters of the project plan given by the design department, we forecast the occurrence quantity of daily peak hours of induced travel in the different functional areas of the building project, attractive quantity of working day morning peak hours, occurrence quantity of rest day evening peak hours, and attractive quantity of rest day evening peak hours, considering the construction area of different functional areas, per unit area residents travel rate, peak hours to increase the number of trips, and other factors. Through analysis and calculation, induced traffic demand can be obtained in the peak hours of building projects. The early peak in the n years is 13,440 (person-time/h). The evening peak in the n year is 13,357 (person-time/h). The early peak in the n+5 year is 15,380 (person-time/h). The evening peak in the n +5 year is 15,472 (person-time/h). The traffic volume in the n year early peak is 2766 (veh/h). The traffic volume in the n year evening peak is 2853 (veh/h). The traffic volume in the n+5 year early peak is 3685 (veh/h). In this paper, the prediction of induced traffic volume of Changchun Kuancheng automobile trade logistics park can provide database for traffic planning and design. It also can effectively avoid the emergence of traffic congestion so as to meet the needs of road traffic in the coming years.

The increase in vehicle volume in Lampung Province has resulted in a decrease in road performance. Capacity evaluation is prevention, determining capacity there are several parameters, one of which is EMP. In this case, the analysis of road section performance refers to MKJI 1997. The characteristics of MKJI 1997 are strongly influenced by the traffic of major cities in Indonesia which are made the place of empirical research in 1991 - 1995. Of course, there are differences in the characteristics that occurred in that year with the current situation. This study was conducted to determine whether there is a difference in the EMP value in MKJI with the current field EMP. This research uses field survey data including traffic volume, road section geometry, and Time Headway data between vehicles. In this study, the emp value is analyzed using the Time Headway method and the data for analyzing the performance of road sections refers to the 1997 Indonesian Road Capacity Manual (MKJI). The results of the emp MC value for the morning and afternoon peak hours on Section 1 are 0.48 and 0.46. Section 2 is 0.46 and 0.44. This value is significantly different from the 1997 MKJI emp of 0.25. for the DS value using emp MKJI on section 1, the results are 0.45 and 0.68. for DS emp calculations on section 0,3 of 0,43 and 1. For section 2 DS emp MKJI of 0.33 and 0.48. For DS emp calculations on section 2 of 0,48 and 0,65. It can be seen that significant differences can be caused by traffic flow conditions, differences in road characteristics, road gemetry conditions, and environmental conditions in 1997 with current conditions. It can also be seen that the greater the emp value will affect the amount of traffic volume so as to make the degree of saturation value greater.

Hotspot identification (HSID) is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB) method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections) similar to the target site from which safety performance functions (SPF) used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering) to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

A System-Wide Adaptive Ramp Metering (SWARM) system has been implemented in the Portland, Oregon metropolitan area, replacing the previous pre-timed ramp-metering system that had been in operation since 1981. SWARM has been deployed on six major corridors and operates during the morning and afternoon peak hours. This report presents results of a "before" and "after" evaluation of the performance of two freeway corridors as part of ongoing efforts to measure the benefits of the new SWARM system, as compared to the pre-timed system. The study benefited from using the existing regional data, surveillance and communications infrastructure in addition to a regional data archive system. The evaluation revealed that the operation of the SWARM system, as currently configured in the Portland metropolitan region, produced mixed results when comparing the selected performance metrics to pre-timed operation. For the I-205 corridor, the results were generally positive. In the morning peak period, SWARM operation resulted in an 18.1% decrease in mainline delay and decreased variability in the delay. For the afternoon peak period, improvements were also found (a 7.9% decrease in mainline delay) with the exception of moderately congested days which saw an 4.7% increase in mainline delay. On the OR-217, however, significant increases were found in overall average delay. In the morning peak period, delay increased 34.9% while in the afternoon period delay increased 55.0%. These conclusions, however, must be tempered because of lack of ramp demand data. If an assumption is made that ramp demand changes correspond with the measured freeway VMT changes, it is likely that ramp delay decreased under SWARM operation (i.e. more vehicles were allowed on the freeway which would equate to lower delay for vehicles on the ramps). Another important finding of this evaluation was that implementation of the SWARM algorithm resulted in significantly more data communication failures in the traffic management system. While this outcome is specific to the ODOT communication infrastructure and hardware, it was not anticipated. These communication failures have the potential to impact other traveler information programs that depend on the freeway surveillance data as well as the SWARM algorithm. Finally, one of the intentions of this research project was to encourage ongoing evaluation and continuous improvement of the ramp metering system and, in general, the overall freeway management system. It is clear from the analysis that meter activation times and rates are necessary to evaluate system performance. Incorporating additional logging capabilities into the

차량의 <TEX>$CO_2$</TEX> 배출량은 통행속도에 따라 다르게 나타난다. 또한, 차량의 통행속도는 도로의 종류나 위치, 시간대, 교통량 등에 따라 다르게 나타난다. 본 논문에서는 주성분분석(PCA : Principal Component Analysis)을 이용하여 간선도로 구간 별 시간대 별로 차량 당 <TEX>$CO_2$</TEX> 다량 배출구간을 판별하여 평가하였다. 분석 결과, 주성분분석 결과 제 1주성분과 제 2주성분으로 성분이 구분되는 것을 알 수 있었고 시간대가 각 주성분을 설명할 수 있는 주요 성분임을 알 수 있었다. 제 1주성분의 경우 새벽시간대와 오후시간대로 주성분을 설명할 수 있었다. 제 2주성분의 경우 오전, 오후 첨두시 시간대로 주성분을 설명할 수 있었다. 그리고 주성분 점수를 산출하여 분석한 결과 제 1주성분의 경우 새벽시간대에도 정체현상이 지속되는 잠원IC~한남대교 구간이 타 구간에 비해 주성분 점수가 높게 나타났고 제 2주성분의 경우 오전,오후 첨두시의 정체현상이 극심한 서울시 접속부와의 이격이 가까운 구간에서 주성분 점수가 높게 나타났다. 결과적으로 주성분 점수를 통하여 차량 당 <TEX>$CO_2$</TEX> 다량 배출 구간을 판별할 수 있었다. <TEX>$CO_2$</TEX> emissions of vehicles vary with vehicle's speeds. In addition, the speeds vary with road type, location, time and traffic volume. In this paper, the section in which a large quantity of <TEX>$CO_2$</TEX> emissions per vehicle is exhausted is determined and analyzed with principal component analysis(PCA). In results of analysis, the principal components analysis were divided into two principal components. It had been identified that the main component was the time zone one which is able to explain each components' role. The first principal component could explain the role of a major component on <TEX>$CO_2$</TEX> emissions per vehicle in the early morning and afternoon hour, respectively. The second principal component could explain the role of the component on <TEX>$CO_2$</TEX> emissions per vehicle in the morning and afternoon peak hours, respectively. Therefore, the section in which a large quantity of <TEX>$CO_2$</TEX> emissions per vehicle could be deterimined by PCA scores.