The measurement of vehicular driving cycle within the city of Edinburgh

In this paper, the development of a driving cycle for the urban area of the city of Edinburgh is presented. The driving cycle was obtained from recorded data in actual traffic conditions, using the car chase technique. A new statistical method of analysing the recorded data was developed. The proposed TRAffic Flow IndeX (TRAFIX) enables the calculation of a representative driving cycle from the various measurements undertaken during two stages of experiments. Data from the City of Edinburgh Council traffic monitoring stations were weighted in proportion to traffic flows on the constituent driving routes. A comparison between the European ECE cycle and the presently proposed Edinburgh driving cycle (EDC) has also been made.     

Real world vehicle emissions: Their correlation with driving parameters

Vehicular population in developing countries is expected to proliferate in the coming decade, centred on Tier II and Tier III cities rather than large metropolis. WLTP is being introduced as a global instrument for emission regulation to reduce gap between standard test procedures and actual road conditions. This work aims at quantifying and discernment of the gap between WLTC and real-world conditions in an urban city in a developing country on the basis of driving cycle parameters and simulated emissions for gasoline fuelled light passenger cars. Real world driving patterns were recorded on different routes and varying traffic conditions using car-chasing technique integrated with GPS monitoring and speed sensors. Real-world driving patterns and ambient conditions were used to simulate emissions using International Vehicle Emissions model for average rate (g/km) and Comprehensive Modal Emissions Model for instantaneous emission (g/s) analysis. Cycle parameters were mathematically calculated to compare WLTC and road trips. The analyses revealed a large gap between WLTC and road conditions. CO emissions were predicted to be 155% higher than WLTC and HC and NOx emissions were estimated to be 63% and 64% higher respectively. These gaps were correlated to different driving cycle parameters. It was observed that road driving occurs at lower average speeds with higher frequency and magnitudes of accelerations. The positive kinetic energy required by road cycles, was 100% higher than WLTC and the Relative Positive Acceleration (RPA) demanded by road cycles, was found to be 60% higher in real-world driving patterns and thereby contribute to higher emissions.     

A robust,data-driven methodology for real-world driving cycle development

This paper develops a robust, data-driven Markov Chain method to capture real-world behaviour in a driving cycle without deconstructing the raw velocity–time sequence. The accuracy of the driving cycles developed using this method was assessed on nine metrics as a function of the number of velocity states, driving cycle length and number of Markov repetitions. The road grade was introduced using vehicle specific power and a velocity penalty. The method was demonstrated on a corpus of 1180 km from a trial of electric scooters. The accuracies of the candidate driving cycles depended most strongly on the number of Markov repetitions. The best driving cycle used 135 velocity modes, was 500 s and captured the corpus behaviour to within 5% after 1,000,000 Markov repetitions. In general, the best driving cycle reproduced the corpus behaviour better when road grade was included.     

Development of a driving cycle for fuel economy in a developing country

A driving cycle corresponding to the driving conditions of a particular country is of decisive importance for fuel economy evaluation of vehicles and automobile engines. The driving pattern was studied in Delhi along four representative routes using a test car equipped with all the instruments required for recording modes of traffic and measuring fuel consumption. An analysis of the field trials results has shown that relative time spent under different modes (cruising, acceleration, etc.) does not practically depend upon a route and rush-or-non rush conditions. Fuel consumption is a function of the average speed and trip length. A four-mode driving cycle has been developed to simulate actual driving conditions with respect to fuel consumption. In comparison with driving cycles of developed countries, the driving cycle has significantly different average speed and relative time spent under acceleration and deceleration. The cycle may be used as a standardized method to evaluate fuel efficiency of vehicles and automobile engines and effect of various gadgets on its improvement.     

Characterization of driving patterns and development of a driving cycle in a military area

This study develops a gradient-sensitive driving cycle for vehicles in military areas with paved and unpaved roads over steep and undulating terrain. The methodology develops the driving cycle using micro-trips extracted from real-world data taking into account factors that affect fuel consumption. The accuracy of cycle depended on the root mean square error and information value.     

Big data driven dynamic driving cycle development for busses in urban public transportation

Field-relevant reference driving cycles, equivalent to real-life operation, are a prerequisite for the consistent development and testing of vehicles, their components, and control algorithms. Furthermore they are the basis for certification and type testing. However, a static cycle can easily be detected during vehicle testing, so that optimized control parameters could be used to obtain improved emission results under test conditions. In this paper, a novel method is described and applied to generate a dynamic driving cycle that statistically matches the real-life operation of a vehicle. The analysis is performed based on an extensive field data set obtained during an automated measurement campaign of public busses for more than a full year with 27,365 h of operation and 315,583 km driven in the city of Hamburg (Germany). The data collected is statistically compared to the static reference cycles New European Driving Cycle (NEDC) and Worldwide harmonized Light Vehicles Test Procedure (WLTP). Two micro trip models with increasing complexity are described and fit to the data set. All models are quantitatively compared to the measured data set applying a Quality of Fit (QoF) indicator. Based on the highest consistency to field data, a non-deterministic driving cycle generator is developed and its output is statistically compared to the original measurement. In contrast to the existing reference cycles, the dynamic output of the non-deterministic driving cycle generator presented in this paper is statistically proven to be consistent with real-life operation of public busses in the urban environment of Hamburg.     

The development of a driving cycle for fuel consumption and emissions evaluation

A technique is developed for synthesising a statistically representative driving cycle for an urban area based on dynamic driving data collected by the chase car technique. The simulation procedure based on a “Knight's Tour” concept relies on an understanding of the dynamics of urban driving, in particular the observed tendency to maintain constant acceleration and deceleration rates. The final synthesised cycle is a representative real driving trace although the analysis raises questions about the level of resolution currently required for driving cycle work.     

A driving cycle detection approach using map service API

Following advancements in smartphone and portable global positioning system (GPS) data collection, wearable GPS data have realized extensive use in transportation surveys and studies. The task of detecting driving cycles (driving or car-mode trajectory segments) from wearable GPS data has been the subject of much research. Specifically, distinguishing driving cycles from other motorized trips (such as taking a bus) is the main research problem in this paper. Many mode detection methods only focus on raw GPS speed data while some studies apply additional information, such as geographic information system (GIS) data, to obtain better detection performance. Procuring and maintaining dedicated road GIS data are costly and not trivial, whereas the technical maturity and broad use of map service application program interface (API) queries offers opportunities for mode detection tasks. The proposed driving cycle detection method takes advantage of map service APIs to obtain high-quality car-mode API route information and uses a trajectory segmentation algorithm to find the best-matched API route. The car-mode API route data combined with the actual route information, including the actual mode information, are used to train a logistic regression machine learning model, which estimates car modes and non-car modes with probability rates. The experimental results show promise for the proposed method’s ability to detect vehicle mode accurately.     

In-use activity measurements for off-road motorcycles and all-terrain vehicles

Off-road recreation vehicles are an important source of emissions and make a disproportionately high contribution to the emissions inventory. Here 58 off-road motorcycles and 39 all-terrain vehicles (ATVs) were instrumented to collect activity data. The activity data for motorcycles and ATVs varied significantly depending on the type of terrain, the type of off highway vehicle, and personal riding habits. The most rapid transitions in speed were typically observed for motorcycles operated on motocross tracks or in trail riding. Over similar terrain, motorcycles had slightly higher top speeds than the ATVs. The highest observed speeds were for motorcycles in open terrain riding conditions. Interestingly, average speeds were comparable between different types of terrain.     

Maneuvers of motorcycles in queues at signalized intersections

The maneuvering models of motorcycles in previous studies often considered motorcycles' traveling in terms of movements in a physical static lane and not in terms of dynamic virtual lane‐based movements. For that reason, these models are not able to imitate motorcyclists' behavior well. This paper proposes a maneuverability model framework for motorcycles in queues at signalized intersections with considering the dynamic motorcycle's lane. The model includes (i) a dynamic motorcycle's lane to identify the current, left, and right lanes of the subject motorcycle, (ii) a threshold distance to determine when a motorcyclist starts to consider maneuvering, (iii) a lane selection model to identify the lane preferred by a motorcyclist, and (iv) a gap acceptance model to describe whether or not the lead and lag gaps are acceptable for maneuvering. The model framework captures the variation across the motorcyclist population and over time observations. The models were applied to Hanoi and Hochiminh city, Vietnam, based on microscopic data collected from video images. All of the parameters were estimated using the maximum likelihood method with the statistical estimation software GAUSS. The results show that 77.88% of the observed maneuvers – either staying in the current lane or turning left or right – could be modeled correctly by the proposed models. Copyright © 2010 John Wiley & Sons, Ltd.     

Inhomogeneous cellular automata modeling for mixed traffic with cars and motorcycles

This paper develops inhomogeneous cellular automata models to elucidate the interacting movements of cars and motorcycles in mixed traffic contexts. The car and motorcycle are represented by non‐identical particle sizes that respectively occupy 6×2 and 2×1 cell units, each of which is 1.25×1.25 meters. Based on the field survey, we establish deterministic cellular automata (CA) rules to govern the particle movements in a two‐dimensional space. The instantaneous positions and speeds for all particles are updated in parallel per second accordingly. The deterministic CA models have been validated by another set of field observed data. To account for the deviations of particles' maximum speeds, we further modify the models with stochastic CA rules. The relationships between flow, cell occupancy (a proxy of density) and speed under different traffic mixtures and road (lane) widths are then elaborated.     

Development of a driving cycle for Colombo,Sri Lanka: an economical approach for developing countries

Financial constraints and lack of availability of traffic‐related information significantly hinder the development of driving cycles in developing countries. This paper proposes an economical, practical, accurate methodology for the development of driving cycles, including the development of a driving cycle for Colombo, Sri Lanka. The proposed methodology captures regional traffic and road conditions and selects a model that represents the collected data sample with minimum available traffic‐related information. Existing methods were modified for route selection by dividing routes into links using nodes or physical junctions to minimize the number of trips required for data collection. Speed–time data for respective links were used to reconstruct speed–time profiles of identified origin–destination pairs. The on‐board method was used for data collection, and the Markov chain theory was used to develop a transition probability matrix of state changes. An additional matrix was introduced to the existing method to improve model representativeness to the collected data sample. Copyright © 2016 John Wiley & Sons, Ltd.     

Road pricing for congestion and accident externalities for mixed traffic of motorcycles and automobiles

Motorcycles play an important role in sharing the trip demand with automobiles for commuting, especially in many cities in Asia. However, the accident cost of a trip by motorcycle is higher than that of an automobile. This study analyzes the road pricing for the congestion and accident externalities of mixed traffic of automobiles and motorcycles. A model for equilibrium trips with no taxation and that for optimal trips with taxation are explored. The model is then applied to the Tucheng City–Banciao City–Taipei central business district corridor in Taipei metropolitan area. The findings in this case study show that the tax for accident externality is larger than that for congestion externality.     

Development of real-world driving cycle: Case study of Pune,India

The critical component of all emission models is a driving cycle representing the traffic behaviour. Although Indian driving cycles were developed to test the compliance of Indian vehicles to the relevant emission standards, they neglects higher speed and acceleration and assume all vehicle activities to be similar irrespective of heterogeneity in the traffic mix. Therefore, this study is an attempt to develop an urban driving cycle for estimating vehicular emissions and fuel consumption. The proposed methodology develops the driving cycle using micro-trips extracted from real-world data. The uniqueness of this methodology is that the driving cycle is constructed considering five important parameters of the time–space profile namely, the percentage acceleration, deceleration, idle, cruise, and the average speed. Therefore, this approach is expected to be a better representation of heterogeneous traffic behaviour. The driving cycle for the city of Pune in India is constructed using the proposed methodology and is compared with existing driving cycles.     

Modelling supported driving as an optimal control cycle: Framework and model characteristics

Driver assistance systems support drivers in operating vehicles in a safe, comfortable and efficient way, and thus may induce changes in traffic flow characteristics. This paper puts forward a receding horizon control framework to model driver assistance and cooperative systems. The accelerations of automated vehicles are controlled to optimise a cost function, assuming other vehicles driving at stationary conditions over a prediction horizon. The flexibility of the framework is demonstrated with controller design of Adaptive Cruise Control (ACC) and Cooperative ACC (C-ACC) systems. The proposed ACC and C-ACC model characteristics are investigated analytically, with focus on equilibrium solutions and stability properties. The proposed ACC model produces plausible human car-following behaviour and is unconditionally locally stable. By careful tuning of parameters, the ACC model generates similar stability characteristics as human driver models. The proposed C-ACC model results in convective downstream and absolute string instability, but not convective upstream string instability observed in human-driven traffic and in the ACC model. The control framework and analytical results provide insights into the influences of ACC and C-ACC systems on traffic flow operations.     

Development of a practical driving cycle construction methodology: A case study in Hong Kong

A practical methodology for constructing a representative driving cycle reflecting the real-world driving conditions is developed for vehicle emissions testing and estimation. The methodology tackles three major tasks, i.e., data collection, route selection and cycle construction. Both car chasing and on-board measurement techniques were employed to collect vehicle speed data. Route selection was based on the records of average annual daily traffic of the road network between major residential areas and commercial/industrial areas. A variety of parameters were employed as the target statistics characterising the driving pattern in the construction of driving cycles. The performance value and speed-acceleration probability distribution were utilised to determine the best synthesised driving cycle. The method is easy to follow and the driving cycles are comparative to other renounced cycles.     

Relationships between characteristics of motorcycles and hydrocarbon emissions in Taiwan: A note

Taiwan’s inspection and maintenance (I/M) programs identifies high-emission motorcycles but, although these help reduce air pollution, they have been criticized for being cost-ineffective. This study examines the relationship between characteristics of motorcycles and hydrocarbon emissions in the Central Air Quality Basin of Taiwan. It is shown that engine size and type, age and manufacturer of a motorcycle significantly affect HC emissions. Larger-size engines emit smaller amounts of HCs; whereas older motorcycles emitted greater amounts. In addition, two-stroke-engine machines produced significantly higher HC emission levels than four-strokes. Variations in HC emissions testing are a result of various I/M testing locations and efficiency may be improved by modifying these.     

Conventional,hybrid and electric vehicles for Australian driving conditions. Part 2: Life cycle CO2-e emissions

This paper is the second of a two part study which quantifies the economic and greenhouse performance of conventional, hybrid and fully electric passenger vehicles operating in Australian driving conditions. This second study focuses on the life cycle greenhouse gas emissions. Two vehicle sizes are considered, Class-B and Class-E, which bracket the large majority of passenger vehicles on Australian roads.Using vehicle simulation models developed in the first study, the trade-offs between the ability of increasingly electric powertrains in curtailing the tailpipe emissions and the corresponding rise in the embedded vehicle emissions have been evaluated. The sensitivity of the life cycle emissions to fuel, electricity and the change in the energy mix are all considered. In conjunction with the total cost of ownership calculated in the companion paper, this allows the cost of mitigating life cycle greenhouse gas emissions through electrification of passenger transport to be estimated under different scenarios. For Class-B vehicles, fully electric vehicles were found to have a higher total cost of ownership and higher life cycle emissions than an equivalent vehicle with an internal combustion engine. For Class-E vehicles, hybrids are found to be the most cost effective whilst also having lowest life cycle emissions under current conditions. Further, hybrid vehicles also exhibit little sensitivity in terms of greenhouse emissions and cost with large changes in system inputs.     

Personalised assistance for fuel-efficient driving

Recent advances in technology are changing the way how everyday activities are performed. Technologies in the traffic domain provide diverse instruments of gathering and analysing data for more fuel-efficient, safe, and convenient travelling for both drivers and passengers. In this article, we propose a reference architecture for a context-aware driving assistant system. Moreover, we exemplify this architecture with a real prototype of a driving assistance system called Driving coach. This prototype collects, fuses and analyses diverse information, like digital map, weather, traffic situation, as well as vehicle information to provide drivers in-depth information regarding their previous trip along with personalised hints to improve their fuel-efficient driving in the future. The Driving coach system monitors its own performance, as well as driver feedback to correct itself to serve the driver more appropriately.     

The market acceptance of electric motorcycles in Taiwan experience through a stated preference analysis

The environmental and energy concerns of using motorcycles in urban areas have fostered the rapid development development of electric motorcycles (EMs) in Taiwan in recent years. EMs' zero-emission, low noise level and high energy efficiency features provide the promising potential to alleviate the severe environmental pollution problem caused by the existing gasoline motorcycles. This study summarizes the recent development of the EM. More specifically, this study aims to analyze the potential demand for EMs based on an interview survey using stated preference modeling approaches. Study results show that female motorists are the potential target market for EMs. Developmental and energy-use issues of EMs are also discussed in this study.