Urban morphogenesis analysis based on geohistorical road data

Road networks result from a subtle balance between geographical coverage and rapid access to strategic points. An understanding of their structure is fundamental when it comes to evaluating and improving territorial accessibility. This study is designed to provide insight into the progressive structuring of territorial patterns by analyzing the evolution of road networks. Studying road network morphogenesis requires geohistorical data, provided here by historical maps from which earlier road networks can be digitized. A hypergraph is constructed from these networks by combining road segments into “ways” on the basis of a method for defining the continuity of road segments. Next, indicators are computed for these ways based on topological and geometrical features. The road patterns of three cities in the Burgundy Franche-Comte region of France (Dijon, Besançon, and Pontarlier) at three historical periods (the 18th, 19th, and twentieth centuries) are then analyzed. In this manner, their topological features and centrality characteristics can be compared from snapshots at different times and places. The innovative method proposed in this paper helps us to read features of the road patterns accurately and to make simple interpretations. It can be applied to any territory for which data is available. The results highlight the underlying structure of the three cities, reveal information about the history and the functioning of the networks, and give preliminary insights into the morphogenesis of those cities. Prospectively this work aims to identify the mechanisms that drive change in road networks. Detecting stability or variation in indicators over time can help in identifying similar behavior, despite geographic and cultural distances, as well as evolution mechanisms linked to specificities of each city. The study of road network morphogenesis can make a major contribution to understanding how road network structure affects accessibility and mobility.

APPLICATION OF KERNEL DENSITY ESTIMATION TO IDENTIFY MOTORCYCLE THEFT HOT SPOTS IN KUCHING, SARAWAK

Motorcycle theft is the most frequently reported cases worldwide, including in Malaysia. This study aims to identify the hot spot areas for motorcycle theft in Kuching. The spatial data include police station sector boundary, road data and latitud and longitude data while attribute data consists of motorcycle theft by year, address of the incident and time. Kernel Density Estimation (KDE) helps to find the hot spot areas of motorcycle theft. Motorcycle theft in Kuching has been reported as more frequent during the day at 54.8% and at 45% during the night from the year 2015 to 2017. Hot spot locations change by year and time. The study found that most of the hot spot areas of motorcycle theft were detected within the Sentral boundary. This indicates that the city centre is an area with a high density of motorcycle theft. This study can help authorities to improve the prevention measures for motorcycle theft while the findings can help in preventing motorcycle theft by police sector boundary.

Vehicle Trajectory Prediction with Lane Stream Attention-Based LSTMs and Road Geometry Linearization

It is essential for autonomous vehicles at level 3 or higher to have the ability to predict the trajectories of surrounding vehicles to safely and effectively plan and drive along trajectories in complex traffic situations. However, predicting the future behavior of vehicles is a challenging issue because traffic vehicles each have different drivers with different driving tendencies and intentions and they interact with each other. This paper presents a Long Short-Term Memory (LSTM) encoder–decoder model that utilizes an attention mechanism that focuses on certain information to predict vehicles’ trajectories. The proposed model was trained using the Highway Drone (HighD) dataset, which is a high-precision, large-scale traffic dataset. We also compared this model to previous studies. Our model effectively predicted future trajectories by using an attention mechanism to manage the importance of the driving flow of the target and adjacent vehicles and the target vehicle’s dynamics in each driving situation. Furthermore, this study presents a method of linearizing the road geometry such that the trajectory prediction model can be used in a variety of road environments. We verified that the road geometry linearization mechanism can improve the trajectory prediction model’s performance on various road environments in a virtual test-driving simulator constructed based on actual road data.

Analysis on Spatial Layout Model of Urban Road Green Landscape Based on Discrete Probability

The development issues about urban road greening design are constantly emerging in modern urban road construction. Therefore, a model of spatial layout of urban road green landscape based on discrete probability was built. The relevant urban road data was collected and the corresponding three-dimensional model of urban road was built. On this basis, the spatial layout and characteristics of urban road were analyzed. According to the analysis results, the greening modes and configuration methods that met the humanistic characteristics were established reasonably. Moreover, the green landscape vegetation was selected in consideration of the growth potential, height and seasonal phase of plants. Then, the discrete probability was used to determine the initial planting location and planting density of vegetation. Finally, following the principle of macro control and micro coordination respectively, the spatial layout of urban road green landscape was achieved from the horizontal and vertical directions. Based on the evaluation for the spatial layout model, it is concluded that the comprehensive score of the designed model is improved by 4.3 points compared with the traditional model.

Quantification Metrics and Analysis of Human Factor, Workload, and Road Infrastructure

This paper describes how human factors (HF) and user workload (WL) can be used by highway designers and traffic engineers to quantify the potential safety of sections of highway. Users’ WL is a quantitative measure of HF. Both HF and WL are used successfully in other fields, such as aviation when pilots have difficulty in using instruments and in touch-down before the start or end of the runway. The traditional highway approach of gauging success is by counting crashes. But with fatalities exceeding 30,000 a year for more than 20 years, the time is right for a new method of analysis. The author has integrated specific WL metrics into a simplified example to aid designers, traffic engineers, and safety analysts in assessing user problems before building new projects or road upgrades. The example uses static and dynamic WL and alternating renewal (AR) metrics (not used by others) to quantify user WL in highway segments for the purpose of illustrating the variation of design and operational safety conditions. The example can be easily modified when new metrics are created, and it illustrates the use of WL and its associated highway safety implications. In short, the approach is based on common sense with trained engineering experience and logic integrated into data-driven safety analyses. The example is a continuation of an earlier FHWA research study illustrating the application of road safety audits and the Interactive Highway Safety Design Model (IHSDM). The IHSDM, Excel, and Google Earth were used because no funding was available for on-road data collection.

Eschatology and the Post-modern Pentecostal Church in Kenya

Purpose: This paper contends that even though the doctrine of eschatology is one of the core teachings of the Christian doctrine, the reality is that it has not always received due attention in the postmodern church. The relegation of this important doctrine to the background of church activity has therefore meant that eschatology has almost been forgotten in church circles. This research sought to establish the interaction of the church with eschatological teachings, beliefs and practices as understood by the pastors, elders/deacons, leaders, and members of Christ Is The Answer Ministries (CITAM). Methodology: The study used mixed methods research design. The target population was the pastors, elders/deacons, leaders, and members of CITAM in two selected assemblies in Nairobi. The assemblies were: CITAM Parklands and CITAM Valley Road. Data was collected from a sample of 68 respondents using stratified sampling technique by use of a questionnaire. The data was then analysed by use of descriptive statistical analysis. Data collected from open-ended sections of the questionnaire were discussed and presented in narrative form. Findings: It was found that the church has done some work as regards to teaching and discussing eschatology. However, the paper also finds that what the church has done to popularize eschatology amongst its members is insufficient and inefficient. More needs to be done in popularizing eschatological themes in church circles. Recommendations: It was recommended that the church should be intentional and committed to doing more in promoting interactions with the doctrine of eschatology. It was also recommended that further studies on the methodologies and ways of systematically inducting eschatology into the everyday church activities and teachings be undertaken.

Penerapan Metode Clustering dengan Fuzzy C-Means untuk Memetakan Daerah Rawan Kecelakaan Lalu Lintas di Surakarta

Along with the development of Surakarta's infrastructure, the need for transportation has also increased. Indirectly, it will also cause several problems that must be considered, such as traffic accidents. Data regarding traffic accidents can be used to classify road sections based on the characteristic similarity factor inherent in the data. The sample data used were 1429 accident data from 89 road data in the city of Surakarta. The clustering method used to get the expected results is the Fuzzy C-Means method. The results of accident data grouping are displayed using tables and maps that describe the mapping of road sections in the jurisdiction of the Surakarta City Police. The variables used to cluster data are the number of events, the number of victims who died, and the number of injured victims. The result of this research is a system application that can classify accident-prone areas using the Fuzzy C-Means method into 3 clusters, where the first cluster consists of 5 data, the second cluster consists of 20 data, and the third cluster consists of 64 data.

Assessment of Incident of Road Traffic Accident along Yola Gombe Route

Road traffic accident has become a prevalent and re-occurring phenomenon in Nigeria which constitutes a menace in modern times. Although all the developed and developing countries have suffer from various degrees of road accidents the developing countries clearly dominates with Nigeria having the second highest road accident among the 193 ranked countries of the world. This research aimed at identifying and assessing road accident hot spots between 2012 and 2016 along Yola/Gombe road. Data was collected from the accident hot spots using GIS/GPS techniques. The data was used to map out these accident hot spots. It also assessed the nature, mode of occurrence and types of injuries associated with the accidents within the study area. It further assesses the types and condition of the vehicle with the highest involvement in the accident within the study area.. GIS and Remote sensing method were used to analyse and georeference the map by throwing the coordinates of the accident hotspots that were picked and Data was also collected from Federal Road Safety Commission. These were analysed statistically. Result was used in this research. The study shows that between 2012 and 2016 about 2846 accidents were witnessed, 1,027 people died, 9017 people sustained various degrees of injuries and 3,384 vehicles were involved in the accidents. The causes of accident were observed. The preventive measures were suggested. The study recommended the need to seriously view road traffic regulations seeing them as human induced problem that demand an urgent attention. Such action is aimed at preventing untimely deaths, social, and economic losses and impacts on average Nigerians.

An Investigation of the Fuel Economy of a Drive Cycle Developed Using the Road Load Energy Criterion

Efforts to mitigate climate change include lowering of greenhouse gas emissions by reducing fuel consumption in the transport sector. Various vehicle technologies and interventions for better fuel economy eventually require chassis dynamometer testing using drive cycles for validation. As such, the methodology to generate these drive cycles from on-road data should produce drive cycles that closely represent actual on-road driving from the fuel economy standpoint. This study presents a comparison of the fuel economy measured from a drive cycle developed using road load energy as a major assessment criterion and the actual on-road fuel economy of a 2013 Isuzu Crosswind utility vehicle used in the UV Express transport fleet in Metro Manila, Philippines. In this approach to drive cycle construction from on-road data, the ratio of the total road load energy of the generated drive cycle to that of the on-road trip is made the same ratio as their respective durations. On-road velocity and fuel consumption were recorded as the test vehicle traversed the 42.5 km. Sucat to Lawton route and vice versa in Metro Manila. Gathered data were processed to generate drive cycles using the modified Markov Chain approach. Three drive cycles of decreasing duration, based on the practicality of testing on a chassis dynamometer, were generated using three arbitrary data compression ratios. These drive cycles were tested using the same vehicle on the chassis dynamometer and compared with the on-road data using road load energy, fuel economy, average speed, and maximum acceleration. For the 893-seconds drive cycle generated, the road load energy error was 3.93% and fuel economy difference of 1.14%. For the 774-seconds cycle generated, the road load energy error was 4.34% and fuel economy difference was 0.91%. For the 664-seconds drive cycle, the road load energy error was 3.68% and fuel economy difference was 0.91%. On-road fuel economy for the 42.5-km. route averaged over nine round trips was 8.785 km/L. Based on the results, the road load energy criterion approach of drive cycle construction methodology can generate drive cycles which can very closely estimate on-road fuel economy.

Fatigue life evaluation of an electrically driven shuttle frame using finite element analysis

This article provides the fatigue life estimation of the frame of a new electrically powered shuttle used in airports and resorts. The frame and components of the electrically powered shuttle were designed by expert employers of the OSCAR Co. using a CATIA computer program. Accelerometers were mounted under and above the prototype electric vehicle frame and external load path interaction data were collected by road tests. By means of five varied degrees of road roughness and three different conditions for vehicle loading, the raw road data were recorded via accelerometers on the vehicle frame. The recorded raw data was made available by processing with the FDesign program. After the external load was determined, a 3D drawing of the frame of the vehicle was transferred to the ANSYS program as a step file. In the chassis model transferred to the ANSYS program; material assignment, meshing of the frame structure, defining the boundary conditions of the structure and static analysis were determined according to the most critical road load, following which fatigue analyses were performed for five different roads. The fatigue life and damage amount of each road on the vehicle frame were calculated separately by considering Miner’s rule according to the S-N high cycle method. Experiments showed that no fatigue damage occurred within the predicted 200,000 km.