Vol. 27 No. 6 (2015)
Borna Abramović, Anna Fraszczyk
2015 (Vol 27), Issue 6
Darja Topolšek, Dejan Dragan
2015 (Vol 27), Issue 6
The goal of the study was to investigate if the drivers behave in the same way when they are driving a motorcycle or a car. For this purpose, the Motorcycle Rider Behaviour Questionnaire and Driver Behaviour Questionnaire were conducted among the same drivers population. Items of questionnaires were used to develop a structural equation model with two factors, one for the motorcyclist’s behaviour, and the other for the car driver’s behaviour. Exploratory and confirmatory factor analyses were also applied in this study. Results revealed a certain difference in driving behaviour. The principal reason lies probably in mental consciousness that the risk-taking driving of a motorbike can result in much more catastrophic consequences than when driving a car. The drivers also pointed out this kind of thinking and the developed model has statistically confirmed the behavioural differences. The implications of these findings are also argued in relation to the validation of the appropriateness of the existing traffic regulations.
Fang Zong, Xiao Sun, Huiyong Zhang, Xiumei Zhu, Wentian Qi
2015 (Vol 27), Issue 6
This study investigates taxi drivers’ multi-day cruising behaviours with GPS data collected in Shenzhen, China. By calculating the inter-daily variability of taxi drivers’ cruising behaviours, the multi-day cruising patterns are investigated. The impacts of learning feature and habitual feature on multi-day cruising behaviours are determined. The results prove that there is variability among taxis’ day-to-day cruising behaviours, and the day-of-week pattern is that taxi drivers tend to cruise a larger area on Friday, and a rather focused area on Monday. The findings also indicate that the impacts of learning feature and habitual feature are more obvious between weekend days than among weekdays. Moreover, learning feature between two sequent weeks is found to be greater than that within one week, while the habitual feature shows recession over time. By revealing taxis' day-to-day cruising pattern and the factors influencing it, the study results provide us with crucial information in predicting taxis' multi-day cruising locations, which can be applied to simulate taxis' multi-day cruising behaviour as well as to determine the traffic volume derived from taxis' cruising behaviour. This can help us in planning of transportation facilities, such as stop stations or parking lots for taxis. Moreover, the findings can be also employed in predicting taxis' adjustments of multi-day cruising locations under the impact of traffic management strategies.
Florin Nemtanu, Ilona Madalina Costea, Catalin Dumitrescu
2015 (Vol 27), Issue 6
The paper is focused on the Fourier transform application in urban traffic analysis and the use of said transform in traffic decomposition. The traffic function is defined as traffic flow generated by different categories of traffic participants. A Fourier analysis was elaborated in terms of identifying the main traffic function components, called traffic sub-functions. This paper presents the results of the method being applied in a real case situation, that is, an intersection in the city of Bucharest where the effect of a bus line was analysed. The analysis was done using different time scales, while three different traffic functions were defined to demonstrate the theoretical effect of the proposed method of analysis. An extension of the method is proposed to be applied in urban areas, especially in the areas covered by predictive traffic control.
Meng Meng, Abdul Ahad Memon, Yiik Diew Wong, Soi Hoi Lam
2015 (Vol 27), Issue 6
A commuter’s mode choice decision in response to provided traveller information is directly dependent on the temporal and spatial interactions between the available travel modes, the network performance and control schemes, and the supplied traveller information. A self-developed simulation model – Intelligent Network Simulation Model (INSIM) – was employed to simulate travel scenarios in a multimodal transportation network. A set of experiments was designed to analyse and evaluate the influence of traffic information on commuter’s mode choice, using a medium-sized area in Singapore. Simulation results showed that the private-to-public mode switch propensity bears a strong and direct relation with amount of disseminated integrated multimodal traveller information (IMTI) as well as timeliness of information update. Other influential factors include degrees of accessibility and compliance to IMTI, and congestion-related events such as accidents.
Anıl İnanlı, Başak Ünsal, Deniz Türsel Eliiyi
2015 (Vol 27), Issue 6
This study considers the distribution network of a well-known perishable food manufacturer and its franchises in Turkey. As the countrywide number of stores is increasing fast, the company is facing problems due to its central distribution of products from a single factory. The objective is to decrease the cost of transportation while maintaining a high level of customer satisfaction. Hence, the focus is on the vehicle routing problem (VRP) of this large franchise chain within each city. The problem is defined as a rich VRP with heterogeneous fleet, site-dependent and compartmentalized vehicles, and soft/hard time windows. This NP-hard problem is modelled and tried with real data on a commercial solver. A basic heuristic procedure which can be used easily by the decision makers is also employed for obtaining quick and high-quality solutions for large instances.
Mian Muhammad Mubasher, Syed Waqar ul Qounain Jaffry
2015 (Vol 27), Issue 6
Urban traffic flow is a complex system. Behavior of an individual driver can have butterfly effect which can become root cause of an emergent phenomenon such as congestion or accident. Interaction of drivers with each other and the surrounding environment forms the dynamics of traffic flow. Hence global effects of traffic flow depend upon the behavior of each individual driver. Due to several applications of driver models in serious games, urban traffic planning and simulations, study of a realistic driver model is important. Hhence cognitive models of a driver agent are required. In order to address this challenge concepts from cognitive science and psychology are employed to design a computational model of driver cognition which is capable of incorporating law abidance and social norms using big five personality profile.
Ivana Šemanjski
2015 (Vol 27), Issue 6
Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data) and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest).
To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes) were achieved for the k-nearest neighbours and random forest techniques.
Ying-En Ge, Olegas Prentkovskis, Chunyan Tang, Wafaa Saleh, Michael G. H. Bell, Raimundas Junevičius
2015 (Vol 27), Issue 6
It is nowadays widely accepted that solving traffic congestion from the demand side is more important and more feasible than offering more capacity or facilities for transportation. Following a brief overview of evolution of the concept of Travel Demand Management (TDM), there is a discussion on the TDM foundations that include demand-side strategies, traveler choice and application settings and the new dimensions that ATDM (Active forms of Transportation and Demand Management) bring to TDM, i.e. active management and integrative management. Subsequently, the authors provide a short review of the state-of-the-art TDM focusing on relevant literature published since 2000. Next, we highlight five TDM topics that are currently hot: traffic congestion pricing, public transit and bicycles, travel behavior, travel plans and methodology. The paper closes with some concluding remarks.
Ferit YAKAR
2015 (Vol 27), Issue 6
In this study, assuming that traffic accident occurrence is determined by some road and environment related factors, and future traffic accidents will occur under the same conditions as past traffic accidents, use of Relative Frequency Method (RFM) (also called frequency ratio method) in the determination of accident-prone road sections is investigated. Method was tested on a highway in Trabzon province of Turkey. At the end of the study, sensitivity and specificity values were calculated as 1.00 and 0.83 respectively, which reflects that the method identified all of the 'accident-prone' sections (there is no false negative) and the method has very strong ability to distinguish 'relatively safe' sections. The most useful property of the method is that, if accident data does not exist due to any reason for some part of the road, method can be still used to identify accident-prone sections by using the road properties.
