Real-World Measurement of Hybrid Buses’ Fuel Consumption and Pollutant Emissions in a Metropolitan Urban Road Network

This study investigates pollutant emissions and fuel consumption of six Euro VI hybrid-diesel public transport buses operating on different scheduled routes in a metropolitan urban road network. Portable emission measurement systems (PEMS) are used in measurements and results are compared to those obtained from a paired number of Euro V conventional buses of the same body type used as control over the same routes. The selected routes vary from urban to highway driving and the experimentation was conducted over the first half of 2015. The available emissions data correspond to a wide range of driving, operating, and ambient conditions. Fuel consumption, distance- and energy-based emission levels are derived and presented in a comparative manner. The effect of different factors, including speed, ambient temperature, and road grade on fuel consumption and emissions performance is investigated. Mean fuel consumption of hybrid buses was found 6.1% lower than conventional ones, from 20% lower up to 16% higher, over six routes tested in total. The mean route difference between the two technologies was not statistically significant. Air conditioning decreased consumption benefits of the hybrid buses. Decrease of the mean route speed from 15 km h−1 tο 8 km h−1 increased the hybrid buses consumption by 63%. Nitrogen oxides (NOx) emissions of the Euro VI hybrid buses were 93 ± 5% lower than conventional Euro V ones. Nitrous oxide (N2O) emissions from hybrid Euro VI buses made up 5.9% of total greenhouse gas emissions and largely offset carbon dioxide (CO2) benefits. The results suggest that hybrid urban buses need to be assessed under realistic operation and environmental conditions to assess their true environmental and fuel consumption benefits.

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Bottleneck Based Gridlock Prediction in an Urban Road Network Using Long Short-Term Memory

Electronics ◽

10.3390/electronics9091412 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1412

Author(s):

Ei Ei Mon ◽

Hideya Ochiai ◽

Chaiyachet Saivichit ◽

Chaodit Aswakul

Keyword(s):

Road Network ◽

Short Term Memory ◽

Road Networks ◽

Sample Sizes ◽

Short Term ◽

Urban Road Network ◽

Term Memory ◽

Urban Road ◽

Wide Range ◽

Long Short Term Memory

The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck indicates the congestion evolution and queue formation, which consequently disturb travel delay and degrade the urban traffic environment and safety. For urban road networks, sensors are needed to cover a wide range of areas, especially for bottleneck and gridlock analysis, requiring high installation and maintenance costs. The emerging widespread availability of GPS vehicles significantly helps to overcome the geographic coverage and spacing limitations of traditional fixed-location detector data. Therefore, this study investigated GPS vehicles that have passed through the links in the simulated gridlock-looped intersection area. The sample size estimation is fundamental to any traffic engineering analysis. Therefore, this study tried a different number of sample sizes to analyze the severe congestion state of gridlock. Traffic condition prediction is one of the primary components of intelligent transportation systems. In this study, the Long Short-Term Memory (LSTM) neural network was applied to predict gridlock based on bottleneck states of intersections in the simulated urban road network. This study chose to work on the Chula-Sathorn SUMO Simulator (Chula-SSS) dataset. It was calibrated with the past actual traffic data collection by using the Simulation of Urban MObility (SUMO) software. The experiments show that LSTM provides satisfactory results for gridlock prediction with temporal dependencies. The reported prediction error is based on long-range time dependencies on the respective sample sizes using the calibrated Chula-SSS dataset. On the other hand, the low sampling rate of GPS trajectories gives high RMSE and MAE error, but with reduced computation time. Analyzing the percentage of simulated GPS data with different random seed numbers suggests the possibility of gridlock identification and reports satisfying prediction errors.

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Macroscopic Relationship between Traffic Condition and Fuel Consumption for an Urban Road Network: Case Study of Beijing

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119853575 ◽

2019 ◽

Vol 2673 (11) ◽

pp. 604-615

Author(s):

Jingyi Wang ◽

Guohua Song ◽

Lei Yu ◽

Hongyu Lu ◽

Jianping Sun ◽

...

Keyword(s):

Fuel Consumption ◽

Road Network ◽

Operating Conditions ◽

Urban Road Network ◽

Traffic Condition ◽

Traffic Demand ◽

Average Speed ◽

Urban Road ◽

Traffic Conditions ◽

Total Fuel Consumption

The waste of fuel causing by traffic congestion is a challenge faced by urban traffic management authorities and travelers. At the same time, massive traffic data allows high-resolution understanding of on-road operating conditions. The development of an algorithm to estimate total fuel consumption from primary traffic condition indices, for example, network average speed, will simplify the evaluation of fuel consumption from the management perspective and guide strategy at the local area level. The objective of this study is to develop a macroscopic relationship between total fuel consumption and the network average speed for an urban road network. Floating car data (FCD) covering 13 weekdays was collected in the field in Beijing, China. FCD from 10 ordinary weekdays are used to develop a quantitative model to define the macroscopic relationship between total fuel consumption and network average speed. The model is then validated by the FCD of the other three weekdays when the traffic demand is low. The average of the resultant absolute relative errors from the validation is found to be 4.65%, which indicates a reasonably high reliability of the developed model under various traffic conditions. The facility- and speed-specific distributions of vehicle kilometers traveled (VKT) are analyzed to explain the macroscopic relationship. The result indicates that the link VKT distribution at different speeds varies greatly when the traffic became congested on expressways. The link VKT distributions are similar for different traffic conditions on arterials and collectors.

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