Jinxian Weng

Vessel collision frequency estimation in the Singapore Strait

This paper aims to estimate Vessel Collision Frequency in the Singapore Strait. This frequency is obtained as the product of the number of Vessel Conflicts and the causation probability using the real-time vessel movement data from the Lloyd’s Marine Intelligence Unit (Lloyd’s MIU) database. The results show that the container carriers have the highest Vessel Collision Frequency while Roll-On Roll-Off (RORO) and passenger ships have the lowest frequency. Tankers cause the highest head-on collision frequency. In the Singapore Strait, the most risky overtaking area is between longitudes 103°48′E and 104°12′E. The most risky head-on area is between longitudes 103°50′E and 104°00′E while the majority of crossing collisions occur between longitudes 103°50′E and 104°12′E. The Vessel Collision Frequency is found to be 1·75 per year in the traffic lanes. Currently, westbound traffic in the Strait is more risky than eastbound traffic (the number of westbound collisions in July was 0·0991 while the number of eastbound collisions was 0·0470). Furthermore, the estimated Vessel Collision Frequency during the day is less than that at night. The results of this paper could be beneficial for the Maritime and Port Authority of Singapore to further enhance the navigational safety strategies implemented in the Singapore Strait. 1. INTRODUCTION. The Singapore Strait is a 105 kilometre long strait between the Strait of Malacca in the west and the South China Sea in the east. It links one of the largest ports to the rest of the world and has a high density of vessel traffic. More than 200 vessels pass through the Strait on a daily basis and this gives an annual throughput of approximately 70,000 vessels, carrying 80% of the oil transported to Northeast Asia, as well as one third of the world’s traded goods including Chinese manufactures, Indonesian coffee etc. Although the Singapore Strait is of great importance to the global economy (Wang and Meng, 2011; Qu and Meng, 2012), it is not deep enough for some of the largest vessels (mostly oil tankers). The Strait also has substantial sections of narrower and shallower shipping lanes. At Philips Channel, it narrows to 2·8 kilometres wide, with 2·1 kilometres in the shipping lanes, creating one of the world’s chokepoints. Therefore, the navigational safety of vessels through the

Evaluation of rear-end crash risk at work zone using work zone traffic data

This paper aims to evaluate the rear-end crash risk at work zone activity area and merging area, as well as analyze the impacts of contributing factors by using work zone traffic data. Here, the rear-end crash risk is referred to as the probability that a vehicle is involved in a rear-end crash accident. The deceleration rate to avoid the crash (DRAC) is used in measuring rear-end crash risk. Based on work zone traffic data in Singapore, three rear-end crash risk models are developed to examine the relationship between rear-end crash risk at activity area and its contributing factors. The fourth rear-end crash risk model is developed to examine the effects of merging behavior on crash risk at merging area. The ANOVA results show that the rear-end crash risk at work zone activity area is statistically different from lane positions. Model results indicate that rear-end crash risk at work zone activity area increases with heavy vehicle percentage and lane traffic flow rate. An interesting finding is that the lane closer to work zone is strongly associated with higher rear-end crash risk. A truck has much higher probability involving in a rear-end accident than a car. Further, the expressway work zone activity area is found to have much larger crash risk than arterial work zone activity area. The merging choice has the dominated effect on risk reduction, suggesting that encouraging vehicles to merge early may be the most effective method to reduce rear-end crash risk at work zone merging area.

A probabilistic quantitative risk assessment model for the long-term work zone crashes.

Work zones especially long-term work zones increase traffic conflicts and cause safety problems. Proper casualty risk assessment for a work zone is of importance for both traffic safety engineers and travelers. This paper develops a novel probabilistic quantitative risk assessment (QRA) model to evaluate the casualty risk combining frequency and consequence of all accident scenarios triggered by long-term work zone crashes. The casualty risk is measured by the individual risk and societal risk. The individual risk can be interpreted as the frequency of a driver/passenger being killed or injured, and the societal risk describes the relation between frequency and the number of casualties. The proposed probabilistic QRA model consists of the estimation of work zone crash frequency, an event tree and consequence estimation models. There are seven intermediate events--age (A), crash unit (CU), vehicle type (VT), alcohol (AL), light condition (LC), crash type (CT) and severity (S)--in the event tree. Since the estimated value of probability for some intermediate event may have large uncertainty, the uncertainty can thus be characterized by a random variable. The consequence estimation model takes into account the combination effects of speed and emergency medical service response time (ERT) on the consequence of work zone crash. Finally, a numerical example based on the Southeast Michigan work zone crash data is carried out. The numerical results show that there will be a 62% decrease of individual fatality risk and 44% reduction of individual injury risk if the mean travel speed is slowed down by 20%. In addition, there will be a 5% reduction of individual fatality risk and 0.05% reduction of individual injury risk if ERT is reduced by 20%. In other words, slowing down speed is more effective than reducing ERT in the casualty risk mitigation.

Analysis of driver casualty risk for different work zone types

Using driver casualty data from the Fatality Analysis Report System, this study examines driver casualty risk and investigates the risk contributing factors in the construction, maintenance and utility work zones. The multiple t-tests results show that the driver casualty risk is statistically different depending on the work zone type. Moreover, construction work zones have the largest driver casualty risk, followed by maintenance and utility work zones. Three separate logistic regression models are developed to predict driver casualty risk for the three work zone types because of their unique features. Finally, the effects of risk factors on driver casualty risk for each work zone type are examined and compared. For all three work zone types, five significant risk factors including road alignment, truck involvement, most harmful event, vehicle age and notification time are associated with increased driver casualty risk while traffic control devices and restraint use are associated with reduced driver casualty risk. However, one finding is that three risk factors (light condition, gender and day of week) exhibit opposing effects on the driver casualty risk in different types of work zones. This may largely be due to different work zone features and driver behavior in different types of work zones.

Decision Tree-Based Model for Estimation of Work Zone Capacity

The ability to estimate work zone capacity accurately is imperative because accurate estimates are a key input to estimates of queue length and traffic delay in work zones. This paper aims to develop a decision tree–based model that considers 16 influencing factors to estimate freeway work zone capacity accurately. The F-test splitting criterion and the postpruning approach are employed to grow and prune the decision tree. Freeway work zone capacity data collected from 14 states and cities are used to train, check, and evaluate the decision tree–based capacity estimation model. Statistical comparison results demonstrate that the decision tree–based model outperforms existing short-term and long-term freeway work zone capacity estimation models, especially when the input values of influencing factors are only partially available for the existing models. A comparison with the Highway Capacity Manual (HCM) also indicates that the decision tree–based model can provide a more accurate estimate of freeway work zone capacity. From the decision tree–based model, traffic engineers can easily estimate work zone capacity for a given freeway work zone by tracing a path down the tree to a terminal node. Because of its accuracy and ease of use, the proposed decision tree–based capacity model is a good alternative for traffic engineers to use in estimating freeway work zone capacity. It is expected that the decision tree–based capacity model could be applied to the HCM chapter on freeway facilities.

Development of a subway operation incident delay model using accelerated failure time approaches.

This study aims to develop a subway operational incident delay model using the parametric accelerated time failure (AFT) approach. Six parametric AFT models including the log-logistic, lognormal and Weibull models, with fixed and random parameters are built based on the Hong Kong subway operation incident data from 2005 to 2012, respectively. In addition, the Weibull model with gamma heterogeneity is also considered to compare the model performance. The goodness-of-fit test results show that the log-logistic AFT model with random parameters is most suitable for estimating the subway incident delay. First, the results show that a longer subway operation incident delay is highly correlated with the following factors: power cable failure, signal cable failure, turnout communication disruption and crashes involving a casualty. Vehicle failure makes the least impact on the increment of subway operation incident delay. According to these results, several possible measures, such as the use of short-distance and wireless communication technology (e.g., Wifi and Zigbee) are suggested to shorten the delay caused by subway operation incidents. Finally, the temporal transferability test results show that the developed log-logistic AFT model with random parameters is stable over time.

Cellular Automata Model for Work Zone Traffic

This paper proposes a cellular automata (CA) model incorporating work zone configuration to model work zone traffic. The randomization probability parameter of the proposed CA model is able to characterize driver acceleration–deceleration behavior. The randomization probability should be a function of traffic flow and work zone configuration that comprises the activity length and transition length; however, past studies have assigned a hypothetical constant value for the randomization probability. This paper calibrates the randomization probability from field data, which is determined by minimizing the square error between simulated travel time and observed travel time by using a trial-and-error method. A polynomial regression method is employed to formulate the randomization probability functions in and outside the work zone. A case study was performed to test the proposed CA model dependent on work zone configuration. Comparison of field data and the proposed CA model for travel time and traffic delay shows very close agreement. Statistical comparison of the simulated results from the proposed CA model and PARAMICS indicates that the proposed CA model performs well in modeling work zone traffic.

Investigation of work zone crash casualty patterns using association rules

Investigation of the casualty crash characteristics and contributory factors is one of the high-priority issues in traffic safety analysis. In this paper, we propose a method based on association rules to analyze the characteristics and contributory factors of work zone crash casualties. A case study is conducted using the Michigan M-94/I-94/I-94BL/I-94BR work zone crash data from 2004 to 2008. The obtained association rules are divided into two parts including rules with high-lift, and rules with high-support for the further analysis. The results show that almost all the high-lift rules contain either environmental or occupant characteristics. The majority of association rules are centered on specific characteristics, such as drinking driving, the highway with more than 4 lanes, speed-limit over 40mph and not use of traffic control devices. It should be pointed out that some stronger associated rules were found in the high-support part. With the network visualization, the association rule method can provide more understandable results for investigating the patterns of work zone crash casualties.

Tree‐Based Logistic Regression Approach for Work Zone Casualty Risk Assessment

This study presents a tree-based logistic regression approach to assessing work zone casualty risk, which is defined as the probability of a vehicle occupant being killed or injured in a work zone crash. First, a decision tree approach is employed to determine the tree structure and interacting factors. Based on the Michigan M-94I-94I-94BLI-94BR highway work zone crash data, an optimal tree comprising four leaf nodes is first determined and the interacting factors are found to be airbag, occupant identity (i.e., driver, passenger), and gender. The data are then split into four groups according to the tree structure. Finally, the logistic regression analysis is separately conducted for each group. The results show that the proposed approach outperforms the pure decision tree model because the former has the capability of examining the marginal effects of risk factors. Compared with the pure logistic regression method, the proposed approach avoids the variable interaction effects so that it significantly improves the prediction accuracy.

In-depth analysis of drivers' merging behavior and rear-end crash risks in work zone merging areas

This study investigates the drivers merging behavior and the rear-end crash risk in work zone merging areas during the entire merging implementation period from the time of starting a merging maneuver to that of completing the maneuver. With the merging traffic data from a work zone site in Singapore, a mixed probit model is developed to describe the merging behavior, and two surrogate safety measures including the time to collision (TTC) and deceleration rate to avoid the crash (DRAC) are adopted to compute the rear-end crash risk between the merging vehicle and its neighboring vehicles. Results show that the merging vehicle has a bigger probability of completing a merging maneuver quickly under one of the following situations: (i) the merging vehicle moves relatively fast; (ii) the merging lead vehicle is a heavy vehicle; and (iii) there is a sizable gap in the adjacent through lane. Results indicate that the rear-end crash risk does not monotonically increase as the merging vehicle speed increases. The merging vehicles rear-end crash risk is also affected by the vehicle type. There is a biggest increment of rear-end crash risk if the merging lead vehicle belongs to a heavy vehicle. Although the reduced remaining distance to work zone could urge the merging vehicle to complete a merging maneuver quickly, it might lead to an increased rear-end crash risk. Interestingly, it is found that the rear-end crash risk could be generally increased over the elapsed time after the merging maneuver being triggered.