Motorway crash duration and its determinants: do durations vary across motorways?

Traffic congestion has been a growing issue in many metropolitan areas during recent years, which necessitates the identification of its key contributors and development of sustainable strategies to help decrease its adverse impacts on traffic networks. Road incidents generally and crashes specifically have been acknowledged as the cause of a large proportion of travel delays in urban areas and account for 25% to 60% of traffic congestion on motorways. Identifying the critical determinants of travel delays has been of significant importance to the incident management systems, which constantly collect and store the incident duration data. This study investigates the individual and simultaneous differential effects of the relevant determinants on motorway crash duration probabilities. In particular, it applies parametric Accelerated Failure Time (AFT) hazard‐based models to develop in‐depth insights into how the crash‐specific characteristic and the associated temporal and infrastructural determinants impact the duration. AFT models with both fixed and random parameters have been calibrated on one year of traffic crash records from two major Australian motorways in South East Queensland, and the differential effects of determinants on crash survival functions have been studied on these two motorways individually. A comprehensive spectrum of commonly used parametric fixed parameter AFT models, including generalized gamma and generalized F families, has been compared with random parameter AFT structures in terms of goodness of fit to the duration data, and as a result, the random parameter Weibull AFT model has been selected as the most appropriate model. Significant determinants of motorway crash duration included traffic diversion requirement, crash injury type, number and type of vehicles involved in a crash, day of week and time of day, towing support requirement and damage to the infrastructure. A major finding of this research is that the motorways under study are significantly different in terms of crash durations; such that motorway 1 exhibits durations that are on average 19% shorter compared with the durations on motorway 2. The differential effects of explanatory variables on crash durations are also different on the two motorways. The detailed presented analysis confirms that looking at the motorway network as a whole, neglecting the individual differences between roads, can lead to erroneous interpretations of duration and inefficient strategies for mitigating travel delays along a particular motorway.     

Clamping performance of fastener in windshield wiper assembly using theoretical and experimental methods

Improper clamping of wiper arms can cause problems in the operation of the wiper. An excessive clamping force can cause damage to the wiper arm head. On the other hand, an insufficient clamping force can cause self-loosening of the nut. Given the lack of direct research on the clamping force of the fastener in wiper assembly, this study verifies the existing clamping performance of the fastener in windshield wiper assembly by theoretical and experimental methods. The theoretical calculation results show that all the clamping performance are satisfied under the general snow load condition. However, under the critical load condition, maximum assembly preload and safety margin against slipping are in disagreement with the standard values. This problem is solved by increasing the strength grade of the bolt. The experimental results show a reducing tendency of the clamping force during the snow durability test. However, this reducing clamping force during the 60,000 test cycles is acceptable. In the case of nut reusing more than two times possibly cause a problem of its loosening because of insufficient clamping force. Therefore, it is recommended that the nut should not be reused more than two times.     

Real-time empirical NO<Subscript>x</Subscript> model based on In-cylinder pressure measurements for light-duty diesel engines

This paper proposes a real-time empirical model of NO_x emissions for diesel engines. The proposed model predicts the level of NO_x emissions using an empirical model developed based on the thermal NO formation mechanism, the extended Zeldovich mechanism. Since it is difficult to consider the exact physical NO formation phenomena in real-time applications, the proposed algorithm adapts the key factors of the NO formation mechanism from the extended Zeldovich mechanism: temperature of the burned gas, concentration of the gas species, and combustion duration where NO is generated. These factors are considered in a prediction model as four parameters: exhaust gas recirculation rate (EGR rate), crank angle location of 50 % of mass fraction burned (MFB50), exhaust lambda value, and combustion acceleration. The proposed prediction model is validated with various steady engine experiments that showed a high linear correlation with the NO_x emission measured by a NO_x sensor. Furthermore, it is also validated for transient experiments.     

Resource-aware integration of AUTOSAR-compliant ECUs with an empirical wcet prediction model

This paper presents an integration method of AUTOSAR-compliant ECUs which can evaluate resource constraints in an early-stage of development. There are three types of resources for an ECU (timing, memory, and interface) which should be carefully managed for successful ECU integration. The proposed method consists of three steps: measurement, prediction, and evaluation. In the first step, a method to measure resource factors for AUTOSAR-compliant software architecture is introduced. Based on the method, a worst-case execution cycle of a runnable, memory section usages of a software component, and interface of legacy ECUs can be obtained. In the second step, the obtained factors are quantitatively predicted according to the architectural designs of the integration ECU. In the case of the timing resource, the worst-case execution time of the integration ECU can be precisely predicted by a proposed empirical model. In the last step, the resource constraints such as CPU, memory, network utilizations can be evaluated with predicted resource factors before implementation. The proposed method was applied to the integration of an in-house engine management system composed of two ECUs. The method successfully provided quantitative measures to evaluate architectural designs of three different integration scenarios.     

The planning of aircraft routes and flight frequencies in an airline network operations

This research is aimed at developing a model that maximizes system profit when determining the aircraft routes and flight frequencies in a network. The model employs network flow techniques to effectively collect or deliver passenger flows from all origins to all destinations using non‐stop and multi‐stop flights in multi‐fleet operations. The model was formulated as a multi‐commodity network flow problem. A Lagrangian‐based algorithm was developed to solve the problem. To test the model in practice, a case study is presented.     

A Trip Reconstruction Tool for GPS-based Personal Travel Surveys

ABSTRACT

This article reports on the development of a trip reconstruction software tool for use in GPS-based personal travel surveys. Specifically, the tool enables the automatic processing of GPS traces of individual survey respondents in order to identify the road links traveled and modes used by each respondent for individual trips. Identifying the links is based on a conventional GIS-based map-matching algorithm and identifying the modes is a rule-based algorithm using attributes of four modes (walk, bicycle, bus and passenger-car). The tool was evaluated using GPS travel data collected for the study and a multi-modal transportation network model of downtown Toronto. The results show that the tool correctly detected about 79% of all links traveled and 92% of all trip modes.     

Comparison study on characteristics of nano-sized particle number distribution by using condensation particle counter calibrated with spray and soot type particle generation methods

In the year 2011, the Particle Measurement Program (PMP) in Europe started the regulation of the diesel vehicle’s nano-sized particle number density (PN) due to its high degree of harm to the human body. Concretely, the standard level of PN emission was introduced in the Euro 5+ and 6 emissions regulation with a limit (<6.0 × 10¹¹#/km) for diesel light-duty vehicle. Therefore, the determination of suitable and sophisticated instruments for reliable particle sampling and analysis was essential in taking exact experimental data. Now, among the PN emission measuring devices suggested by the PMP, condensation particle counter (CPC) is a key equipment for measuring the particle number density in real time and it has been used extensively. However, CPC can cause different results depending on operating conditions of the saturator and condensation that induce different rates of particle growth. This study was conducted to analyze the effect of CPC calibrated by a two-particle generator with spray and soot type methods applied on the nano-sized particle distribution’s parameters such as number concentration and linearity. Also, in order to ensure the reliability for particle sensor system named as PPS, which had emerged as a useful diagnostic to making spatially and temporally resolved quantitative measurements of diesel PN concentration, it was compared with calibrated CPC system. As a result, nano-sized particle measuring system with CPC calibrated by spray type particle generator had a much higher counting efficiency, indicating a larger nano size available than soot type particle generator. And, comparative experimental results on the correlation between the particle number of CPC to a reflectance PPS system showed that above 5,000 #/cm ³ in number concentrations measured by CPC as well as PPS were found to be similar with good linear relationship.     

Fuel economy comparison of conventional drive trains series and parallel hybrid electric step vans

A hybrid electric vehicle (HEV) is a vehicle that combines a conventional propulsion system with an on-board rechargeable energy storage system to achieve better fuel economy than a conventional vehicle HEVs do not have limited ranges like battery electric vehicles, which use batteries charged by an external source. The different propulsion power systems may have common subsystems or components. The objective of this study is to compare the fuel economies of a conventional step van, a series hybrid electric step van (HESV), and a parallel HESV by calculating the fuel consumption using the ADVISOR software by NREL. We also showed the results of the vehicles in different driving cycles including the Central Business District bus cycles, the New York City Cycle, and the US EPA City and Highway cycles.     

Fault detection method for electric parking brake (EPB) systems with sensorless estimation using current ripples

A fault detection method with parity equations is proposed in this paper. Due to its low cost implementation, the velocity of the motor is not measurable in electric parking brake (EPB) systems. Therefore, residuals are not reliable when estimating the motor velocity with a low-resolution encoder. In this paper, we propose a fault detection method with sensorless estimation using current ripples that estimates the position and velocity of the motor by detecting periodical oscillations of the armature current caused by rotor slots. In addition, this method can estimate the position and velocity of the motor with less computational effort than a state observer. Moreover, the method is less sensitive to motor parameters than model-based estimation methods. The effectiveness of this method is validated with experimental data, and the simulation results show that various faults have their own residual patterns. Therefore, we can detect the presence of faults by monitoring the residual signals.     

Association rule learning to improve deficiency inspection in port state control

ABSTRACT

The inspection of foreign ships in national ports is a critical measure in port state control (PSC), preventing substandard ships from entering national ports. Multifarious inspection items, limited inspection time and inspector manpower are challenging PSC inspection. This research applies data mining to analyze historical PSC inspection records in Taiwan’s major ports to extract potential valuable information for PSC onboard inspections. Using the Apriori Algorithm, the analysis identifies many useful association rules among PSC deficiencies in terms of specific ship characteristics, such as ship types, societies, and flags. The general rules identified show that the items ‘Water/Weathertight conditions’ and ‘Fire safety’ are significantly related. Besides, in the analysis of the various ship types, several different rules are found. After comparing the analysis of ship types and ship societies, it can be observed that the association rules for specific ship types, such as oil tankers, have a better effect than those for individual ship societies do. These identified rules can not only help inspectors effectively spot the associated deficiencies, but also improve the efficiency of PSC inspection. The ports other than Taiwan’s ports can apply a similar analysis method to identify corresponding association rules suitable for their own inspections.