Aggregate expenditure elasticity for transport and communication in Australia

For the purpose of estimating Engel elasticity, one needs to find the best functional form among various alternatives. In this paper, a new function, which is called the double semi-log Engel function turns out to be the best functional form for the transport and communication items in Australia on the basis of the distance function (D²) criterion, and the non-nested hypothesis testing procedure. This function is then used to estimate total expenditure elasticity, and the percentage change in consumer demand due to changes in total expenditure and total expenditure inequalities; using the 1975–76 Household Expenditure Survey data. The results of the analyses demonstrate that transport and communication is a necessary item in Australia on the basis of two criteria, viz., its elasticity is not significantly greater than unity, and the percentage change in demand increases with a decrease of the total expenditure inequality [emphasised by Iyengar (I960)]. Policy implication of the analyses has also been discussed.     

Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems

Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81 m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services.     

Integrated analysis of land-use and transport policy interventions

Analysing the impact of urban policy interventions on urban growth, land use and transport (LUT) is crucial for urban planners, transport planners and policy-makers, especially in rapidly growing cities. This paper presents a cellular automata-based land-use/transport interaction model – Metronamica-LUTI – for Jeddah that is used to analyse the impact of different proposed policy interventions under two urban growth scenarios for the period 2011–2031. Used as an integrated policy impact assessment tool, the model demonstrates a strong reciprocal relationship between LUT in Jeddah. This study shows that relevant spatial information and integrated policy impact assessment can provide rich insights into the interaction between LUT, the appropriate policy to consider in place and time which traditional planning practice and typical static urban models cannot do.     

Crash prediction model for two-lane rural highways in the Ashanti region of Ghana

Crash Prediction Models (CPMs) have been used elsewhere as a useful tool by road Engineers and Planners. There is however no study on the prediction of road traffic crashes on rural highways in Ghana. The main objective of the study was to develop a prediction model for road traffic crashes occurring on the rural sections of the highways in the Ashanti Region of Ghana. The model was developed for all injury crashes occurring on selected rural highways in the Region over the three (3) year period 2005–2007. Data was collected from 76 rural highway sections and each section varied between 0.8 km and 6.7 km. Data collected for each section comprised injury crash data, traffic flow and speed data, and roadway characteristics and road geometry data. The Generalised Linear Model (GLM) with Negative Binomial (NB) error structure was used to estimate the model parameters. Two types of models, the ‘core’ model which included key exposure variables only and the ‘full’ model which included a wider range of variables were developed. The results show that traffic flow, highway segment length, junction density, terrain type and presence of a village settlement within road segments were found to be statistically significant explanatory variables (p < 0.05) for crash involvement. Adding one junction to a 1 km section of road segment was found to increase injury crashes by 32.0% and sections which had a village settlement within them were found to increase injury crashes by 60.3% compared with segments with no settlements. The model explained 61.2% of the systematic variation in the data. Road and Traffic Engineers and Planners can apply the crash prediction model as a tool in safety improvement works and in the design of safer roads. It is recommended that to improve safety, highways should be designed to by-pass village settlements and that the number of junctions on a highway should be limited to carefully designed ones.     

Practical approach for travel time estimation from point traffic detector data

Accurate estimation of travel time is critical to the success of advanced traffic management systems and advanced traveler information systems. Travel time estimation also provides basic data support for travel time reliability research, which is being recognized as an important performance measure of the transportation system. This paper investigates a number of methods to address the three major issues associated with travel time estimation from point traffic detector data: data filling for missing or error data, speed transformation from time‐mean speed to space‐mean speed, and travel time estimation that converts the speeds recorded at detector locations to travel time along the highway segment. The case study results show that the spatial and temporal interpolation of missing data and the transformation to space‐mean speed improve the accuracy of the estimates of travel time. The results also indicate that the piecewise constant‐acceleration‐based method developed in this study and the average speed method produce better results than the other three methods proposed in previous studies. Copyright © 2011 John Wiley & Sons, Ltd.     

Predicting arterial breakdown probability: A data mining approach

Predicting the probability of traffic breakdown can be used as an important input for creating advanced traffic management strategies that are specifically implemented to reduce this probability. However, most, if not all, past research on the probability of breakdown has focused on freeways. This study focuses on the prediction of arterial breakdown probability based on archived traffic data for use in real-time transportation system operations. The breakdown of an arterial segment is defined in this study as a segment's operating condition under the level of service F according to the highway capacity manual threshold, although any other level of service could be used. Data from point detection and automatic vehicle identification matching technologies are aggregated in space and time to allow their use as inputs to the prediction model. A decision tree approach, combined with binary logistic regression, is used in this study to predict the breakdown probability based on these inputs. The model is validated using data not used in the development of the model. The research shows that the root mean square error and the mean absolute error of the prediction was 13.6 and 11%, respectively. The analysis also shows that the best set of parameters used in the prediction can be different for different links, due to the various causes of breakdown and characteristics of different links. Predicting the probability of breakdown in ahead of time will allow the agencies to change the signal-timing plan that can delay or eliminate the breakdown.     

Roll-induced bifurcation in ship maneuvering under model uncertainty

In this paper, a mathematical model is developed for the maneuvering motion of a naval ship and bifurcations of its equilibrium are identified in roll-coupled motion. The subject ship is a high-speed surface combatant with twin-propeller twin-rudder system. Captive model tests are conducted for the ship using planar motion mechanism. Maneuvering coefficients are calculated by polynomial curve fitting of the test data. Uncertainty distribution in the coefficients is assumed same as that of the curve fitting errors. Uncertainty in the model coefficients is propagated to full-scale simulation results by the stochastic response surface method (SRSM). This method is computationally efficient as compared to standard Monte Carlo simulation technique. The SRSM uses polynomial chaos expansion of orthogonal to fit any probability distribution. Bifurcation analysis of the mathematical model is performed by varying the vertical center of gravity as the bifurcation parameter. Hopf bifurcation is identified. It is found that the bifurcations occur due to the coupling of roll motion with sway, yaw motion and rudder angle. In the presence of wind, roll angle response in bifurcation diagram is discussed.     

Calculation of external costs for freight transport

Abstract

The negative impacts of transport are in general associated with costs. These costs are usually denoted as ‘external costs’ or ‘externalities’. This paper presents a tool for calculating external costs for freight transport together with its application to a number of case studies. The categories considered include: air pollution, greenhouse gases, noise, accidents and congestion. Results are presented for a number of different transport alternatives as total costs and divided into categories. The uncertainties in the results are discussed. The assessment of these costs is essential for predicting future transport costs.     

The vehicle emissions and performance monitoring system: analysis of tailpipe emissions and vehicle performance

This paper describes tailpipe emission results generated by the Vehicle Performance and Emissions Monitoring system (VPEMS). VPEMS integrates on‐board emissions and vehicle/driver performance measurements with positioning and communications technologies, to transmit a coherent spatio‐temporally referenced dataset to a central base station in near real time. These results focus on relationships between tailpipe emissions of CO, CO₂, NO_x and speed and acceleration. Emissions produced by different driving modes are also presented. Results are generally as one would expect, showing variation between vehicle speed, vehicle acceleration and emissions. Data is based upon a test run in central London on urban streets with speeds not exceeding about 65 km/h. The results presented demonstrate the capabilities of the system. Various issues remain with regard to validation of the data and expansion of the system capability to obtain additional vehicle performance data.     

Quantifying the transport-related impacts of parental school choice in England

School travel is becoming increasingly car-based and this is leading to many environmental and health implications for children all over the world. One of several reasons for this is that journey to school distances have increased over time. This is a trend that has been reinforced in some countries by the adoption of so-called ‘school choice’ policies, whereby parents can apply on behalf of their child(ren) to attend any school, and not only the school they live closest to. This paper examines the traffic and environmental impacts of the school choice policy in England. It achieves this by analysing School Census data from 2009 from the Department for Education. Multinomial logit modelling and mixed multinomial logit modelling are used to illustrate the current travel behaviour of English children in their journey to school and examine how there can be a significant reduction in vehicle miles travelled, CO₂ emissions and fuel consumption if the ‘school choice’ policy is removed. The model shows that when school choice was replaced by a policy where each child only travelled to their ‘nearest school’ several changes occurred in English school travel. Vehicle Miles Travelled (VMT) by motorised transport fell by 1 % for car travel and 10 % for bus travel per day. The reduction in vehicle miles travelled could lead to less congestion on the roads during the morning rush hour and less cars driving near school gates. Mode choice changed in the modelled scenario. Car use fell from 32 to 22 %. Bus use fell from 12 to 7 %, whilst NMT saw a rise of 17 %. With more children travelling to school by walking or cycling the current epidemic of childhood obesity could also be reduced through active travel.