981 research outputs found
Estimating Passenger Car Equivalent Factors for Heterogeneous Traffic Using Occupancy-Density Linear Regression Model
No full textA variety of methods have been proposed in the existing literature for the estimation of passenger car equivalent (PCE) factors. These methods are based on the comparison of selected attributes of different vehicles. This research, for the first time, utilizes the basic notion of the linear relationship between road area occupancy and density for the estimation of PCE factors for different vehicle types in heterogeneous traffic. Aerial photographs obtained from an unmanned aerial vehicle (UAV) were analyzed to estimate the road area occupancy and the number of vehicles classified in seven selected groups. A linear least-squares regression model was developed between road area occupancy and classified vehicle count. The coefficients of the occupancy-density linear regression model were used to estimate PCE and motorcycle equivalent (MCE) factors. The comparison of the estimated set of PCE values with the values reported in the literature shows that PCE factors estimated using the proposed method are reasonable and produce a better occupancy-density relationship than the other studies. In comparison with the existing methods that rely on lane-based measurements, the proposed method is well suited for traffic with weak/no lane discipline, as it considers the entire road width and the dynamics of lateral movement of different types of vehicles. The proposed method does not need extensive traffic data of speeds, headways, flow rates, and so forth, and is applicable on aerial photographs obtained from other sources, such as satellites.Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported with funding from Exascale Open Data Analytics Lab, National Center for Big Data and Cloud Computing (NCBC) and the Higher Education Commission of Pakistan.
Acknowledgments
The authors are thankful to research students Syed Hassan Ali, Haseeb Ahmed, Zohaib Ahmed, Aqib Abbasi, Asad Rehan, Mirza Ali Haider, Syed Abbas Hasan Zaidi, and Omema for their help in this research
sj-docx-1-onc-10.1177_11795549221084832 – Supplemental material for Body Mass Index and Diabetes Mellitus May Predict Poorer Overall Survival of Oral Squamous Cell Carcinoma Patients: A Retrospective Cohort From a Tertiary-Care Centre of a Resource-Limited Country
No full textSupplemental material, sj-docx-1-onc-10.1177_11795549221084832 for Body Mass Index and Diabetes Mellitus May Predict Poorer Overall Survival of Oral Squamous Cell Carcinoma Patients: A Retrospective Cohort From a Tertiary-Care Centre of a Resource-Limited Country by Yumna Adnan, Syed Muhammad Adnan Ali, Muhammad Sohail Awan, Nida Zahid, Muhammad Ozair Awan, Hammad Afzal Kayani and Hasnain Ahmed Farooqui in Clinical Medicine Insights: Oncology</p
Heterogeneity in behavioural response to pricing policies in the transition from motorcycles to private cars in motorcycle-based societies
No full textPricing instruments are widely seen as an effective tool for reducing the travel demand for private vehicles. In contrast to developed countries, the design of pricing policies in certain developing countries is more challenging, owing to the mixed use of private cars and motorcycles. This study argues for the existence of a transitional group of motorcycle users who will switch to being car users. An investigation of the behavioural responses to a pricing policy from private car users and motorcycle users is implemented in Ho Chi Minh City, Vietnam. A propensity score-matching technique is used to identify the transitional group. The results regarding the mode choice models for various pricing policies show similar responses between the transitional motorcycle users and car users. Such characteristics of the transitional group imply that ignorance of travellers' heterogeneity may cause significant bias, especially when modelling pricing policies.This research was financed by the Special Research Fund of Hasselt University.
Financial support in data collection: Ho Chi Minh City Institute for Development Studies (HIDS)
Author contribution: The authors confirm contribution to the paper as follows: study concept and design: Hoang Thuy Linh, Nguyen Hoang Tung, Vu Anh Tuan, Muhammad Adnan, and Tom Bellemans; data preparation, analysis, and interpretation of results: Hoang Thuy Linh; draft manuscript preparation: Hoang Thuy Linh, Nguyen Hoang Tung, and Muhammad Adnan. All authors reviewed the results and approved the final version of the manuscript
A Comprehensive Modelling Framework to Integrate External Trips in a Travel Demand Model
No full textTravel demand modeling studies often require external trips data for the model validation. This paper presents a framework to incorporate nonresident external trips within a travel demand model (TDM), whether it is a four-step model or an activity-based travel framework. This paper first describes the framework components for defining an external region and a series of interlinked models that are joined in a developed simulation platform to estimate external trips. Then, generation and distribution models for external-external (EE), external-internal (EI) and also nonresident non-home-based (NR-NHB) trips are described. The models also take into account the time-of-day (TOD) component for three essential motives: (1) to achieve more consistency in the origin-destination (OD) matrix of the external trips with the OD matrix of the internal trips for route assignment; (2) omitting the need to fit the TOD curves to disaggregate trips from 24-h trip tables into respective periods; and (3) providing an opportunity to better estimate NR-NHB trips which depend on the TOD because these trips are performed far from the home location and the number of such trips may decrease as the time progresses in a day, especially after midday. Furthermore, to minimize the need for any extra data collection, the models were developed using household travel survey data and opensource land-use data. The results suggest the suitability of the data sources to model external trips. Furthermore, the TOD-related variables, including its interaction effect with other explanatory variables, were found to be significant in many models, which signifies the importance of including a TOD component in modeling external trips. Model validation also confirmed the soundness of the proposed models. The paper, therefore, is beneficial for the practitioner community to estimate external trips and incorporate them in the travel demand model estimation. (c) 2019 American Society of Civil Engineers
Activity-based model for medium-sized cities considering external activity–travel: Enhancing FEATHERS framework
No full textTravel demand modeling has evolved from the traditional four-step models to tour-based models which eventually became the basis of the advanced Activity-Based Models (ABM). The added value of the ABM over others is its ability to test various policy scenarios by considering the complete activity–travel pattern of individuals living in the region. However, the majority of the ABM restricts residents’ activities within the study area which results in distorted travel patterns. The external travel is modeled separately via external models which are insensitive to policy tests that an ABM is capable of analyzing. Consequently, to minimize external travel, transport planners tend to define a larger study area. This approach, however, requires huge resources which significantly deterred the worldwide penetration of ABM. To overcome these limitations, this study presents a framework to model residents’ travel and activities outside the study area as part of the complete activity–travel schedule. This is realized by including the Catchment Area (CA), a region outside the study area, in the destination choice models. Within the destination choice models, a top-level model is introduced that specifies for each activity its destination inside or outside the study area. For activities to be performed inside the study area, the detailed land use information is utilized to determine the exact location. However, for activities in the CA, another series of models are presented that use land use information obtained from open-source platforms in order to minimize the data collection efforts. These modifications are implemented in FEATHERS, an ABM operational for Flanders, Belgium and the methodology is tested on three medium-sized regions within Flanders. The results indicate improvements in the model outputs by defining medium-sized regions as study areas as compared to defining a large study area. Furthermore, the Points of Interests (POI) density is also found to be significant in many cases. Lastly, a comprehensive validation framework is presented to compare the results of the ABM for the medium-sized regions against the ABM for Flanders. The validation includes the (dis)aggregate distribution of activities, trips, and tours in time, space and structure (e.g. transport modes used and types of activities performed) through eleven measures. The results demonstrate similar distributions between the two ABM (i.e. ABM for medium-sized regions and for Flanders) and thus confirms the validity of the proposed methodology. This study, therefore, shall lead to the development of ABM for medium-sized regions.Part of this research was funded by Higher Education Commission (HEC) of Pakistan
sj-docx-2-npx-10.1177_1934578X211031148 - Supplemental material for Antileishmanial Potential of Berberine Alkaloids From <i>Berberis glaucocarpa</i> Roots: Molecular Docking Suggests Relevant <i>Leishmania</i> Protein Targets
No full textSupplemental material, sj-docx-2-npx-10.1177_1934578X211031148 for Antileishmanial Potential of Berberine Alkaloids From Berberis glaucocarpa Roots: Molecular Docking Suggests Relevant Leishmania Protein Targets by Muhammad Alamzeb, Saqib Ali, Mamoon-Ur-Rashid, Behramand Khan, Ihsanullah, Adnan, Muhammad Omer, Asad Ullah, Javed Ali, William N. Setzer, Syed M. Salman, Ajmal Khan and Akram Shah in Natural Product Communications</p
sj-docx-1-npx-10.1177_1934578X211031148 - Supplemental material for Antileishmanial Potential of Berberine Alkaloids From <i>Berberis glaucocarpa</i> Roots: Molecular Docking Suggests Relevant <i>Leishmania</i> Protein Targets
No full textSupplemental material, sj-docx-1-npx-10.1177_1934578X211031148 for Antileishmanial Potential of Berberine Alkaloids From Berberis glaucocarpa Roots: Molecular Docking Suggests Relevant Leishmania Protein Targets by Muhammad Alamzeb, Saqib Ali, Mamoon-Ur-Rashid, Behramand Khan, Ihsanullah, Adnan, Muhammad Omer, Asad Ullah, Javed Ali, William N. Setzer, Syed M. Salman, Ajmal Khan and Akram Shah in Natural Product Communications</p
Effect of sidewalk vendors on pedestrian movement characteristics: A microscopic simulation study of Addis Ababa, Ethiopia
No full textStreet vending is an essential part of the urban informal economy in developing countries. Related to rapid urbanization and socio-economic challenges, studies have proposed comprehensive street design and management that accommodate sidewalk vendors efficiently. To this end, this paper evaluated the effect of three alternatives to integrate sidewalk vendors on average pedestrian density. A social-force based pedestrian micro-simulation model (PTV-Viswalk-11) was calibrated using a macroscopic approach. A calibrated model was then used to evaluate the effect of a kiosk, a sidewalk vendor in frontage, and a sidewalk vendor in furniture zones on pedestrian movement under different scenarios. Results indicated that the average pedestrian density varied with the location and width of the vending stall, width of the walkway, pedestrian flow and the presence of a customer interacting with a vendor. The paper concludes with recommendations for planners and local authorities.Hagos, KG (corresponding author), Hasselt Univ, Dept Transportat Sci, B-3590 Diepenbeek, Belgium ; Hasselt Univ, Transportat Res Inst IMOB, B-3590 Diepenbeek, Belgium
[email protected]; [email protected]; [email protected]
A personalized mobility based intervention to promote pro-environmental travel behavior
No full textThe development of effective behavioral change strategies that will convince individuals to voluntarily switch to pro-environmental travel behavior is a core research area for transportation and environmental experts. Personalized travel planning (PTP) is considered as an effective approach that encourages individuals to adopt environmental-friendly travel choices. This paper describes the design and implementation of a PTP intervention that was developed based on the persuasive techniques and the stage model of self-regulated behavior change (SSBC). Based on the recorded travel diary of the individuals, web-based customized pro-environmental travel plans were suggested along with pro-environmental and pro-healthy impacts. The effectiveness was assessed by comparing the travel behavior along with consequences before and after the implemented intervention. Significant differences were observed in an individual travel behavior regarding car dependency and active mobility with an effect size of 0.28 and 0.45 (Cohen's d) respectively. On an average, 4.25 percentage points decrease in CO2 emission and 6.10 percentage point increase in physical activity level per individual was found due to their change in travel behavior. Stage analysis of the individual travel behavior revealed that the implemented PTP intervention triggers an individual's transition towards more action-oriented stages in this behavior change process. Based on the results, it is concluded that intervention is effective to promote pro-environmental and pro-healthy travel choices and can bring higher benefits when implemented on a broader level.This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 689954. This paper reflects the authors views. The European Commission is not liable for any use that may be made of the information contained therein.Adnan, M (corresponding author), Hasselt Univ, Transportat Res Inst IMOB, Agoralaan, B-3590 Diepenbeek, Belgium.
[email protected]; [email protected]; [email protected]; [email protected]
Random forest models for motorcycle accident prediction using naturalistic driving based big data
No full textMotorcycle accident studies usually rely upon data collected from road accidents collected through questionnaire surveys/police reports including characteristics of motorcycle riders and contextual data such as road environment. The present study utilizes big data, in the form of vehicle trajectory patterns collected through GPS, coupled with self-reported road accident information along with motorcycle rider characteristics to predict the likelihood of involvement of a motorcyclist in an accident. Random Forest-based machine learning algorithm is employed by taking inputs based on a variety of features derived from trajectory data. These features are mobility-based features, acceleration event-based features, aggressive overtaking event-based features and motorcyclists socio-economic features. Additionally, the relative importance of features is also determined which shows that aggressive overtaking event-based features have more impact on motorcycle accidents as compared to other categories of features. The developed model is useful in identifying risky motorcyclists and implementing safety measures focused towards them
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