Cellular automata modeling of pedestrian＇s crossing dynamics

Cellular automata modeling techniques and the characteristics of mixed traffic flow were used to derive the 2-dimensional model presented here for simulation of pedestrian’s crossing dynamics. A conception of “stop point” is introduced to deal with traffic obstacles and resolve conflicts among pedestrians or between pedestrians and the other vehicles on the crosswalk. The model can be easily extended, is very efficient for simulation of pedestrian’s crossing dy- namics, can be integrated into traffic simulation software, and has been proved feasible by simulation experiments.

Optimization of Crosswalk Width Based on Bi-Directional Pedestrian Crossing Time at Signalized Intersections

The effects of the interactions between bi-directional pedestrians on the crossing time and the crosswalk width are studied. Firstly,the crossing process of bi-directional pedestrians is analyzed.The total crosswalk time is divided into a discharge time and a crossing time. The interactions between bi-directional pedestrians are quantified with the drag force theory. Then,a model is developed to study the crossing time based on the kinetic energy theory and momentum theory. Subsequently,the related parameters of the proposed model are calibrated with observed information. The relationships among crosswalk width,signal time,pedestrian volume and level of service are simulated with the proposed model. The results are verified and compared with other models. The proposed model has an absolute value of relative error of 9. 38%,which is smaller than that of the Alhajyaseen model（ 15. 26%） and Highway Capacity Manual（ HCM） model（ 12. 42%）. Finally,suggested crosswalk widths at different conditions are successfully estimated with the proposed crossing time model.

Recognition of occluded pedestrians from the driver's perspective for extending sight distance and ensuring driving safety at signal-free intersections

Urban intersections without traffic signals are prone to accidents involving motor vehicles and pedestrians.Utilizing computer vision technology to detect pedestrians crossing the street can effectively mitigate the occurrence of such accidents.Faced with the complex issue of pedestrian occlusion at signal-free intersections,this paper proposes a target detection model called Head feature And ENMS fusion Residual connection For CNN(HAERC).Specifically,the model includes a head feature module that detects occluded pedestrians by integrating their head features with the overall target.Additionally,to address the misselection caused by overlapping candidate boxes in two-stage target detection models,an Extended Non-Maximum Suppression classifier(ENMS)with expanded IoU thresholds is proposed.Finally,leveraging the CityPersons dataset and categorizing it into four classes based on occlusion levels(heavy,reasonable,partial,bare),the HAERC model is experimented on these classes and compared with baseline models.Experimental results demonstrate that HAERC achieves superior False Positives Per Image(FPPI)values of 46.64%,9.59%,9.43%,and 6.78%respectively for the four classes,outperforming all baseline models.The study concludes that the HAERC model effectively identifies occluded pedestrians in the complex environment of urban intersections without traffic signals,thereby enhancing safety for long-range driving at such intersections.

Analysis of dynamic features in intersecting pedestrian flows

This paper focuses on the simulation analysis of stripe formation and dynamic features of intersecting pedestrian flows.The intersecting flows consist of two streams of pedestrians and each pedestrian stream has a desired walking direction.The model adopted in the simulations is the social force model, which can reproduce the self-organization phenomena successfully. Three scenarios of different cross angles are established. The simulations confirm the empirical observations that there is a stripe formation when two streams of pedestrians intersect and the direction of the stripes is perpendicular to the sum of the directional vectors of the two streams. It can be concluded from the numerical simulation results that smaller cross angle results in higher mean speed and lower level of speed fluctuation. Moreover, the detailed pictures of pedestrians＇ moving behavior at intersections are given as well.

Assessing Factors for Occurrence of Road Accidents in Tanzania Using Panel Data Analysis: Road Safety Perspective

This study assessed critical factors for road traffic accidents and associated mitigation to reduce the accidents by the year 2030. The study was guided by research questions, what are the major causatives of road accidents and how to mitigate the problem. The study used secondary data collected from the repository database of traffic police at the division of Tanzania Road Safety Squad. Data were collected at the events of accident occurrences and reported annually by regions. Panel data analysis was used to allow for controlling variables which cannot be observed over time and across areas such as regions. Pooled Poisson model, fixed effect and random effect Poisson model was applied to assess factors for road traffic accidents. Fixed effect model was the best model with a reasonably good fit. Results indicated that all predictors are significant under fixed effect Poisson model with a p-value less than 0.05 but Passengers and Railway crossing road was found insignificant and dropped in the final model. Laws and regulatory frameworks should be formulated and enforced promptly for Tanzania may reach the target of 2nd decade of action for roads safety 2021-2030.

Equity issues and the PeCUS index:an indirect analysis of community severance

Urban barriers are often a consequence of the unplanned expansion of transport infrastructures,housing growth nearby existing transport infrastructures,and the vehicular traffic itself.This process,also known as“community severance”,is a problem that affects the travel patterns of the population and causes negative impacts to adjacent communities,but it is not easy to assess.As an alternative to the costly technologies and restricted-access data sometimes used to analyze urban barriers,we propose an analytical approach that assesses community severance levels by classifying the quality of Pedestrian Crossings on Urban Streets(the PeCUS index).Additionally,the approach is used to identify possible inequities nearby the different classification groups regarding demographic data.We used the chi-square(χ^(2))test of independence and the standardized Pearson residuals to indicate deficits and excesses of residents regarding the demographic characteristics studied surrounding the urban streets.We found the following evidences of associations:residents with low-income or those aged up to 19 tend to live close to the worst crossings,whereas residents with permanent mobility constraints or the elderly tend to live near the crossings with the best classifications.Therefore,the study shows that the assessment of community severance by the analysis of the PeCUS index is feasible.Moreover,it reveals that the distribution of residents surrounding the distinct classes of quality of crossings is equitable for vulnerable social groups,except for low-income or young residents.