Hussain Hamid

Complex Network Theory Applied to the Growth of Kuala Lumpur’s Public Urban Rail Transit Network

Recently, the number of studies involving complex network applications in transportation has increased steadily as scholars from various fields analyze traffic networks. Nonetheless, research on rail network growth is relatively rare. This research examines the evolution of the Public Urban Rail Transit Networks of Kuala Lumpur (PURTNoKL) based on complex network theory and covers both the topological structure of the rail system and future trends in network growth. In addition, network performance when facing different attack strategies is also assessed. Three topological network characteristics are considered: connections, clustering and centrality. In PURTNoKL, we found that the total number of nodes and edges exhibit a linear relationship and that the average degree stays within the interval [2.0488, 2.6774] with heavy-tailed distributions. The evolutionary process shows that the cumulative probability distribution (CPD) of degree and the average shortest path length show good fit with exponential distribution and normal distribution, respectively. Moreover, PURTNoKL exhibits clear cluster characteristics; most of the nodes have a 2-core value, and the CPDs of the centrality’s closeness and betweenness follow a normal distribution function and an exponential distribution, respectively. Finally, we discuss four different types of network growth styles and the line extension process, which reveal that the rail network’s growth is likely based on the nodes with the biggest lengths of the shortest path and that network protection should emphasize those nodes with the largest degrees and the highest betweenness values. This research may enhance the networkability of the rail system and better shape the future growth of public rail networks.

Integration of Ant Colony Optimization and Object-Based Analysis for LiDAR Data Classification

Light detection and ranging (LiDAR) data classification provides useful thematic maps for numerous geospatial applications. Several methods and algorithms have been proposed recently for LiDAR data classification. Most studies focused on object-based analysis because of its advantages over per-pixel-based methods. However, several issues, such as parameter optimization, attribute selection, and development of transferable rulesets, remain challenging in this topic. This study contributes to LiDAR data classification by developing an approach that integrates ant colony optimization (ACO) and rule-based classification. First, LiDAR-derived digital elevation and digital surface models were integrated with high-resolution orthophotos. Second, the processed raster was segmented with the multiresolution segmentation method. Subsequently, the parameters were optimized with a supervised technique based on fuzzy analysis. A total of 20 attributes were selected based on general knowledge on the study area and LiDAR data; the best subset containing 12 attributes was then selected via ACO. These attributes were utilized to develop rulesets through the use of a decision tree algorithm, and a thematic map was generated for the study area. Results revealed the robustness of the proposed method, which has an overall accuracy of ∼95% and a kappa coefficient of 0.94. The rule-based approach with all attributes and the k nearest neighbor (KNN) classification method were applied to validate the results of the proposed method. The overall accuracy of the rule-based method with all attributes was ∼88% (kappa = 0.82), whereas the KNN method had an overall accuracy of <70% and produced a poor thematic map. The selection of the ACO algorithm was justified through a comparison with three well-known feature selection methods. On the other hand, the transferability of the developed rules was evaluated by using a second LiDAR dataset at another study area. The overall accuracy and the kappa index for the second study area were 92% and 0.90, respectively. Overall, the findings indicate that the selection of a subset with significant attributes is important for accurate LiDAR data classification with object-based methods.

Motorcyclist Rear Brake Simple Perception–Response Times in Rear-End Collision Situations

Objective: The purpose of this study was to determine the baseline motorcycle riders’ perception–response times (PRTs) in an expected object braking task and to determine the significant difference between PRTs of older and younger riders. Methods: Fifty-nine participants sat on their motorcycles in exactly the same way as they would when riding and then they awaited activation of the taillights of the passenger car (parked) in front of them. PRTs of the motorcyclists were transcribed from the camcorder when the riders hit the brakes as quickly as possible following the activation of the cars brake lights. Results: Results of PRT were calculated by taking the average of both male and female older and younger riders. The study demonstrates that the mean and standard deviation of the motorcycle baseline PRTs are 0.44 and 0.11 s, respectively. Riders’ age and gender were not found to be significant variables for PRT. Conclusion: The mean of baseline perception–reaction time of motorcycle riders is smaller than that of passenger car drivers. If traffic facilities are designed based on passenger car drivers’ simple perception–reaction times where drivers are generally more alert (for example, in traffic signal design), they can provide the required PRT for motorcyclists. This suggests that the utilization of more powerful brake lights on motorcycles could be highly effective for preventing rear-end motorcycle collisions.

Motorcycle Characteristics for Sight Distance Investigation on Exclusive Motorcycle Lanes

In developing Association of Southeast Asian Nations (ASEAN) countries, the motorcycle is a popular means of transportation because it is cheap and provides flexible door-to-door mobility. However, motorcyclists are also highly involved in road accidents. Separating motorcycles from other vehicles in traffic by providing motorcycle lanes is a good engineering measure to improve the safety of motorcyclists. In designing motorcycle lanes, considerations of geometrical elements such as horizontal and vertical curve lengths, stopping distances, and passing sight distances are essential. This study attempts to quantify the eye levels and head levels of motorcycle riders and motorcycle headlight and taillight height characteristics that influenced these geometrical elements. Characteristics of the motorcycles observed along the existing exclusive motorcycle lanes in Selangor state of Malaysia were transcribed from a camcorder, using reference dimension. Findings recommend a design motorcycle eye height of 1,350 mm (5th percentile), headlight height of 800 mm (5th percentile), and taillight height of 625 mm (5th percentile). A motorcyclist height of 1,525 mm (10th percentile) is recommended for the design of sight distance. Recommended heights reduce the cost of motorcycle lane construction with demonstrated safety compared with the current criteria.

Role of sensory and cognitive conspicuity in the prevention of collisions between motorcycles and trucks at T-intersections

Motorcyclists are particularly vulnerable to injury in crashes with heavy vehicles due to substantial differences in vehicle mass, the degree of protection and speed. There is a considerable difference in height between motorcycles and trucks; motorcycles are viewed by truck drivers from downward angles, and shorter distances between them mean steeper downward angles. Hence, we anticipated that the effects of motorcycle conspicuity treatments would be different for truck drivers. Therefore, this study aims to evaluate the effects of motorcycle conspicuity treatments on the identification and detection of motorcycles by truck drivers. Two complementary experiments were performed; the first experiment assessed the impact of motorcycle sensory conspicuity on the ability of un-alerted truck drivers to detect motorcycles, and the second experiment assessed the motorcycle cognitive conspicuity to alerted truck drivers. The sensory conspicuity was measured in terms of motorcycle detection rates by un-alerted truck drivers when they were not anticipating a motorcycle within a realistic driving scene, while the cognitive conspicuity was determined by the time taken by alerted truck drivers to actively search for a motorcycle. In the first experiment, the participants were presented with 10 pictures and were instructed to report the kinds of vehicles that were presented in the pictures. Each picture was shown to the participants for 600ms. In the second experiment, the participants were presented with the same set of pictures and were instructed to respond by clicking the right button on a mouse as soon as they detected a motorcycle in the picture. The results indicate that the motorcycle detection rate increases, and the response time to search for a motorcycle decreases, as the distance between the targeted motorcycle and the viewer decreases. This is true regardless of the type of conspicuity treatment used. The use of daytime running headlights (DRH) was found to increase the detection rate and the identification of a motorcycle by a truck driver at a farther distance, but effect deteriorates as the distance decreases. The results show that the detection rate and the identification of a motorcyclist wearing a black helmet with a reflective sticker increases as the distance between the motorcycle and the truck decreases. We also found that a motorcyclist wearing a white helmet and a white outfit is more identifiable and detectable at both shorter and longer distances. In conclusion, although this study provides evidence that the use of appropriate conspicuity treatments enhances motorcycle conspicuity to truck drivers, we suggest that more attention should be paid to the effect of background environment on motorcycle conspicuity.

CHARACTERIZATION OF EFFECTIVE TIRE CONTACT AREA FOR VARIOUS TREAD PATTERNS

The effective tire-pavement contact area affects the relative damage of asphalt pavement and should be incorporated in both mechanistic and empirical response analyses of pavements. A new machine called ROTOCOM Wheel Tracker (RCWT) was designed and fabricated to capture the effective tire contact area apart from slab compacting, and conducting simulative laboratory wheel tracking tests. The main focus of this paper is laboratory measurement of effective tire contact areas for various tread patterns. Seven tire treads were selected for the footprint image analyses at five tire loads and four tire inflation pressures. An image processing MATLAB-based program was coded to calculate the contact areas of the 280 imprints obtained from both sides of the RCWT. Factorial analysis indicated significant effects of tire tread, tire load, and inflation pressure on the resulting contact area. Comparison between effective and traditional contact areas indicated that the current pavement design procedure with traditional circular contact area extremely overestimates the actual tire-pavement contact area up to 92%.

Effects of parking and cordon charge policies on tourist's modal shift in central business district of Mashhad city, Iran.

The second largest holy city of the world, Mashhad, attracts high volumes of tourists and pilgrims every year. Most visitors travel by private car and are a source of considerable funds for the local economy. Among road users, tourists as one of the major traveller categories in Mashhad city behave differently due to the particular trip purpose. The aim of this research is to model tourists shifting modes of travel behaviour when policy measures, such as the parking and cordon fares, are implemented. The tourists’ preferences were examined using binary logit analysis when different options of travel cost and time scenarios were provided. Results indicate that travel time, parking cost, cordon cost, education level and vehicle price influence tourists modal choice. In addition, the finding shows that congestion pricing will be more effective than a parking pricing strategy in encouraging switching of modes.

Applications of Deep Learning in Severity Prediction of Traffic Accidents

This study investigates the power of deep learning in predicting the severity of injuries when accidents occur due to traffic on Malaysian highways. Three network architectures based on a simple feedforward Neural Networks (NN), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN) were proposed and optimized through a grid search optimization to fine tune the hyperparameters of the models that can best predict the outputs with less computational costs. The results showed that among the tested algorithms, the RNN model with an average accuracy of 73.76% outperformed the NN model (68.79%) and the CNN (70.30%) model based on a 10-fold cross-validation approach. On the other hand, the sensitivity analysis indicated that the best optimization algorithm is “Nadam” in all the three network architectures. In addition, the best batch size for the NN and RNN was determined to be 4 and 8 for CNN. The dropout with keep probability of 0.2 and 0.5 was found critical for the CNN and RNN models, respectively. This research has shown that deep learning models such as CNN and RNN provide additional information inherent in the raw data such as temporal and spatial correlations that outperform the traditional NN model in terms of both accuracy and stability.

Modeling tourist's mode choice behavior under parking and cordon charge policies

Traffic congestion is a perennial problem at seasonal tourist destinations throughout the world. Meanwhile, understanding the mode choices of tourists traveler will help to identify effective solutions. This analysis contributes to that effort by characterizing the mode preferences of tourists under parking and cordon polices in central business district (CBD) of Mashhad city. With respect to the research methodology, this research employs econometric model, discrete choice model. Using a stated preference survey of visitors, the results presented that travel time, parking fare, cordon cost, education levels and vehicle price type are characteristics in Mashhad city that determine tourists modal choice and influence the modal shift from congestion pricing in central business area. In addition, the finding shows that congestion pricing has more effective response than the parking strategy.