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

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

Energies ◽

10.3390/en11102569 ◽

2018 ◽

Vol 11 (10) ◽

pp. 2569 ◽

Cited By ~ 12

Author(s):

Christos Keramydas ◽

Georgios Papadopoulos ◽

Leonidas Ntziachristos ◽

Ting-Shek Lo ◽

Kwok-Lam Ng ◽

...

Keyword(s):

Fuel Consumption ◽

Road Network ◽

Pollutant Emissions ◽

Emission Measurement ◽

Body Type ◽

Urban Road Network ◽

Urban Road ◽

Wide Range ◽

The Mean ◽

Consumption Benefits

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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Short-Term traffic condition prediction of urban road network based on improved SVM

2017 International Smart Cities Conference (ISC2) ◽

10.1109/isc2.2017.8090856 ◽

2017 ◽

Cited By ~ 7

Author(s):

He Yan ◽

Dong-Jun Yu

Keyword(s):

Road Network ◽

Short Term ◽

Urban Road Network ◽

Traffic Condition ◽

Urban Road

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Iterative Learning Control with Forgetting Factor for Urban Road Network

Journal of Control Science and Engineering ◽

10.1155/2017/9269187 ◽

2017 ◽

Vol 2017 ◽

pp. 1-7 ◽

Cited By ~ 6

Author(s):

Tianyi Lan ◽

Fei Yan ◽

Hui Lin

Keyword(s):

Traffic Congestion ◽

Iterative Learning Control ◽

Road Network ◽

Learning Control ◽

Iterative Learning ◽

Control Input ◽

Forgetting Factor ◽

Urban Road Network ◽

Traffic Condition ◽

Urban Road

In order to improve the traffic condition, a novel iterative learning control (ILC) algorithm with forgetting factor for urban road network is proposed by using the repeat characteristics of traffic flow in this paper. Rigorous analysis shows that the proposed ILC algorithm can guarantee the asymptotic convergence. Through iterative learning control of the traffic signals, the number of vehicles on each road in the network can gradually approach the desired level, thereby preventing oversaturation and traffic congestion. The introduced forgetting factor can effectively adjust the control input according to the states of the system and filter along the direction of the iteration. The results show that the forgetting factor has an important effect on the robustness of the system. The theoretical analysis and experimental simulations are given to verify the validity of the proposed method.

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