Evaluation of Applicability and Accuracy of Bus Travel Time Prediction in High and Low Frequency Bus Routes Using Tree-Based ML Techniques

Prediction of bus travel time is a key component of an intelligent transportation system and has many benefits for both service users and providers. Although there is a rich literature on bus travel prediction, some limitations can still be observed. First, high-frequency and low-frequency bus routes have different characterizations in both operational and passenger behavior aspects. Therefore, it is highly expected that bus travel time prediction methods for different frequencies must have different outputs. Second, in the era of big data, applications of machine learning (ML) techniques in travel time prediction have significantly increased. However, there is no single ML model introduced in the literature that is the most accurate in predicting bus travel, especially with regard to bus service frequency. Consequently, the main objective of this study is to determine the most applicable route construction approach and most accurate tree-based ML technique for predicting bus travel time on high- and low-frequency bus routes. The following tree-based ML techniques were adopted in this study: chi-square automatic interaction detection (CHAID), random forest (RF), and gradient-boosted tree (GBT). According to the results, CHAID was selected as the most accurate model for predicting travel time on high-frequency routes, while GBT showed the best performance for low-frequency service. CHIAD analysis identified distance between stops and terminal departure behavior as the most significant factors of travel time on high-frequency routes. Moreover, we introduced the "key stop-based" route construction method for the first time, which is an accurate, reliable, and applicable method

Sequential algorithm to stratify liver fibrosis risk in overweight/obese metabolic dysfunction-associated fatty liver disease

BackgroundNon-diabetic overweight/obese metabolic dysfunction-associated fatty liver disease (MAFLD) represents the largest subgroup with heterogeneous liver fibrosis risk. Metabolic dysfunction promotes liver fibrosis. Here, we investigated whether incorporating additional metabolic risk factors into clinical evaluation improved liver fibrosis risk stratification among individuals with non-diabetic overweight/obese MAFLD.Materials and methodsComprehensive metabolic evaluation including 75-gram oral glucose tolerance test was performed in over 1000 participants from the New Hong Kong Cardiovascular Risk Factor Prevalence Study (HK-NCRISPS), a contemporary population-based study of HK Chinese. Hepatic steatosis and fibrosis were evaluated based on controlled attenuation parameter and liver stiffness (LS) measured using vibration-controlled transient elastography, respectively. Clinically significant liver fibrosis was defined as LS ≥8.0 kPa. Our findings were validated in an independent pooled cohort comprising individuals with obesity and/or polycystic ovarian syndrome.ResultsOf the 1020 recruited community-dwelling individuals, 312 (30.6%) had non-diabetic overweight/obese MAFLD. Among them, 6.4% had LS ≥8.0 kPa. In multivariable stepwise logistic regression analysis, abnormal serum aspartate aminotransferase (AST) (OR 7.95, p<0.001) and homeostasis model assessment of insulin resistance (HOMA-IR) ≥2.5 (OR 5.01, p=0.008) were independently associated with LS ≥8.0 kPa, in a model also consisting of other metabolic risk factors including central adiposity, hypertension, dyslipidaemia and prediabetes. A sequential screening algorithm using abnormal AST, followed by elevated HOMA-IR, was developed to identify individuals with LS ≥8.0 kPa, and externally validated with satisfactory sensitivity (>80%) and negative predictive value (>90%).ConclusionA sequential algorithm incorporating AST and HOMA-IR levels improves fibrosis risk stratification among non-diabetic overweight/obese MAFLD individuals

The ASIASAFE road safety handbook: the best practices in traffic safety between Europe – Indonesia, Malaysia, and Vietnam

This handbook on Road Traffic Safety, titled "The ASIASAFE Road Safety Handbook: The Best Practices in Traffic Safety between Europe – Indonesia, Malaysia, and Vietnam," is a collaborative effort involving nine universities across Asia and Europe. It represents over three years of intensive research, discussions, and consultations with relevant agencies in participating countries. The six Asian universities involved are the Malaysia University of Science and Technology, Universiti Malaya (Malaysia), Universitas Gadjah Mada, Universitas Muhammadiyah (Indonesia), and Nguyen Tat Thanh University, University of Transport and Communications (Vietnam). The three European universities are Linkoping University (Sweden), University of Porto (Portugal), and University of Rome "Tor Vergata" (Italy). While every effort has been made to ensure the accuracy and relevance of the information provided in this handbook, it is essential to acknowledge that each country has its own unique conditions and circumstances concerning road traffic safety. Therefore, the content of this handbook should be adopted and adapted according to the specific situations and needs of individual countries. Readers are advised to exercise caution and discretion in implementing the recommendations and strategies outlined in this handbook, considering the local context and consulting with relevant authorities and experts as needed. The authors and contributing institutions do not accept any responsibility for the consequences of actions taken based on the information provided in this handbook

Investigation on Motorcyclist Riding Behaviour at Curve Entry Using Instrumented Motorcycle

This paper details the study on the changes in riding behaviour, such as changes in speed as well as the brake force and throttle force applied, when motorcyclists ride over a curve section road using an instrumented motorcycle. In this study, an instrumented motorcycle equipped with various types of sensors, on-board cameras, and data loggers, was developed in order to collect the riding data on the study site. Results from the statistical analysis showed that riding characteristics, such as changes in speed, brake force, and throttle force applied, are influenced by the distance from the curve entry, riding experience, and travel mileage of the riders. A structural equation modeling was used to study the impact of these variables on the change of riding behaviour in curve entry section. Four regression equations are formed to study the relationship between four dependent variables, which are speed, throttle force, front brake force, and rear brake force applied with the independent variables

Analysis of Motorcyclist Riding Behaviour on Speed Table

This paper focuses on the study of the change of various types of riding behaviour, such as speed, brake force, and throttle force applied, when they ride across the speed table. An instrumented motorcycle equipped with various types of sensor, on-board camera, and data logger was used in acquiring the traffic data in the research. Riders were instructed to ride across two speed tables and the riding data were then analyzed to study the behaviour change from different riders. The results from statistical analysis showed that the riding characteristics such as speed, brake force, and throttle force applied are influenced by distance from hump, riding experience, and travel mileage of riders. Riders tend to apply higher brake intensity at distance point 50 m before the speed table and release the braking at point −10 m after the hump. In short, speed table has different rates of influence towards riding behaviour on different factors, such as distance from hump and different riders’ attributes

Exclusive Bus Lane Allocation Considering Multimodal Traffic Equity Based on Bi-Level Programming

To ensure the equity of exclusive bus lane (EBL) allocation under multimodal traffic conditions, a bi-level programming model is first constructed. The upper-level model is the minimum total system cost considering the Gini coefficient and the lower-level model constructed a stochastic user equilibrium (SUE) model based on logit loading. Secondly, a heuristic algorithm combining an improved genetic algorithm (GA) and a method of the successive average method (MSA) is designed. Finally, the Nguyen and Dupuis networks are used as examples to verify and analyze the effectiveness, superiority and sensitivity of the model and algorithm. The results show that the method can effectively obtain the optimal solution of the upper-level model as 15,004 RMB, the Gini coefficient is 0.31, and the equity is at a relatively reasonable level. Compared with the different allocation schemes, the proposed scheme has a higher bus sharing rate and lower Gini coefficient. At the same time, when the actual demand is twice the basic demand, the bus share rate is the largest, 65%, and the Gini coefficient is the smallest at 0.3. The bus share rate decreases with the increase in the proportion of high time value travelers, which fully verifies the sensitivity of the model to the type of traveler

Exclusive Bus Lane Allocation Considering Multimodal Traffic Equity Based on Bi-Level Programming

To ensure the equity of exclusive bus lane (EBL) allocation under multimodal traffic conditions, a bi-level programming model is first constructed. The upper-level model is the minimum total system cost considering the Gini coefficient and the lower-level model constructed a stochastic user equilibrium (SUE) model based on logit loading. Secondly, a heuristic algorithm combining an improved genetic algorithm (GA) and a method of the successive average method (MSA) is designed. Finally, the Nguyen and Dupuis networks are used as examples to verify and analyze the effectiveness, superiority and sensitivity of the model and algorithm. The results show that the method can effectively obtain the optimal solution of the upper-level model as 15,004 RMB, the Gini coefficient is 0.31, and the equity is at a relatively reasonable level. Compared with the different allocation schemes, the proposed scheme has a higher bus sharing rate and lower Gini coefficient. At the same time, when the actual demand is twice the basic demand, the bus share rate is the largest, 65%, and the Gini coefficient is the smallest at 0.3. The bus share rate decreases with the increase in the proportion of high time value travelers, which fully verifies the sensitivity of the model to the type of traveler

Using simulation model as a tool for analyzing bus service reliability and implementing improvement strategies.

Bus services naturally tend to be unstable and are not always capable of adhering to schedules without control strategies. Therefore, bus users and bus service providers face travel time variation and irregularity. After a comprehensive review of the literature, a significant gap was recognized in the field of public transportation reliability. According to literature, there is no consistency in reliability definition and indicators. Companies have their own definition of bus service reliability, and they mostly neglect the passengers' perspective of reliability. Therefore, four reliability indicators were selected in this study to fill the gap in the literature and cover both passengers' and operators' perceptions of reliability: waiting time and on-board crowding level from passengers' perspective, and headway regularity index at stops (HRIS) and bus bunching/big gap percentage from operators' perspective. The primary objective of this research is to improve the reliability of high frequency of bus service and simulation tools currently being used by the public transportation companies. Therefore, a simulation model of bus service was developed to study the strategies to alleviate it. Four different types of strategies were selected and implemented according to Route U32 (Kuala Lumpur) specifications. Model out-put showed that control strategies such as headway-based dispatching could significantly improve headway regularity by almost 62% and the waiting time by 51% on average. Both holding strategies at key stops (previous and Prefol holding) have shown an almost similar impact on reliability indicators. Waiting time was reduced by 44% and 43% after the previous and Prefol Headway strategies were adopted, respectively. However, the implementation of the component of headway-based strategies at the terminal and key stops showed the best impact on reliability, in terms of passenger waiting time. Waiting time and excess waiting time were both significantly reduced by 52.86% and 81.44%, respectively. Nevertheless, the strategies did not show any significant positive effect on the level of crowding during morning peak hours

Utilisation of Recycled Concrete Aggregates for Sustainable Porous Asphalt Pavements

The use of recycled concrete aggregates (RCA) for porous asphalts is a viable attempt towards waste management and sustainable conservation of natural resources. Installation of a porous asphalt wearing course is justified in highway pavements because it offers higher skid resistance, glare reduction, lesser traffic noise, reduction of hydroplaning, and mitigation of urban heat island phenomenon. The performance of porous asphalt mixtures containing 0%, 20%, 40%, 60%, 80% and 100% of coarse RCA as replacement for granite was studied and reported in this paper. The mixture containing 0% RCA was used as the control. The skid properties, permeability, water susceptibility and mechanical behaviour of the mixtures under various loading conditions were investigated. Blending granite and RCA in the porous asphalt mixture gave better Indirect Tensile Strength (ITS), rutting resistance, and impact strength indicators. The mixture with 60% RCA achieved desirable results in all tests. It exhibited the best performance based on its ITS and impact strength of 431 kPa and 380 J, respectively. These values were higher than the control by 3% and 30%, respectively. Utilisation of RCA in porous asphalt pavements is recommended based on the results of this study

Characterization of pervious concrete with blended natural aggregate and recycled concrete aggregates

The utilization of recycled coarse aggregates (RCA) from construction and demolition wastes to produce green concrete serves as a sustainable solution with manifold environmental benefits. This study aims to widen the potential uses of RCA to fabricate pervious concrete for non-structural applications. The mechanical and surface properties, permeability, and greenhouse gases assessment of pervious concrete made with blended normal granite aggregates and RCA were investigated in this work. RCA replacement levels of 20%, 40%, 60%, 80% and 100% were used. Experimental results show that the RCA mixes have lower mechanical properties. Microscopic analyses show that the lower strength of RCA mixes was attributed to the failure path of RCA specimens which occurred at the weaker adhered mortar on RCA. Despite the drop in strength, all RCA-mixes attained the minimum BS EN 1338 requirements in terms of skid and abrasion resistance. In the mix with 100% RCA, the waste content in the pervious concrete mix was 87% by weight and 73% by volume. The greenhouse gases assessment also shows that the CO2 emission of this 100% RCA mix is 24% lower than the control mix