^{1}

^{2}

^{3}

^{2}

^{4}

The authors have declared that no competing interests exist.

Conceived and designed the experiments: L-NW Y-SC J-WC. Performed the experiments: L-NW Y-SC. Analyzed the data: L-NW Y-SC. Contributed reagents/materials/analysis tools: L-NW Y-SC H-SZ. Wrote the paper: L-NW Y-SC.

The calculating method for fuel consumption (

Statistically, one-third of the gasoline was exhausted by autos worldwide each year; and energy supply had become one of huge challenges for human beings. Consequently, 3 main fuel consumption reduction technologies were put forward and implemented from the aspects of vehicles, driving behaviors and traffic management and control respectively. Above all, advanced engine technologies, which were usually improved by auto makers and researchers, were gradually applied including hybrid electric engine, and turbocharger technology. For instance, hybrid electric vehicles (HEVs) or plug-in hybrid electric vehicles (PHEVs) were proved to be successful to reduce the fuel consumption; many relative strategies and methods were proposed to save the HEVs fuel consumption. Hu

Then, eco-driving is encouraged and can reduce fuel consumption accordingly. Of course, some assistance system should be used. Staubach

Finally, the strategies and methods of traffic management and control can be implemented to economize fuel consumption. This is the focus of this article. A number of earlier research findings revealed that the fuel consumption per unit distance can be approximately described as a linear function of the average speed and stop frequency [

However, the fuel consumption on intersection area is completely different with common urban road segments. Here are 3 primary kinds of advanced control strategies we can conclude. For a start, traffic engineers or researchers put forward relative methods to analyze or reduce the fuel consumption on intersection involving vehicle running state analysis, traffic design, traffic management and control and traffic flow investigation. Luo set up vehicle fuel consumption models based on vehicle running state data by simulating at intersections. It shows that the fuel consumption is mainly affected by travel speed, stop rate and delay [

Unfortunately, little information has focus on the vehicle fuel consumption influenced by the existence of the intersection in urban arterial road directly. It has been approved that the impact of the intersection on the vehicular operating is significant in the urban arterial road. How does it affect the fuel consumption of the vehicle exactly? This paper tries to use the detailed on-site test to illustrate this point. Therefore, the core interest of this paper lies in problems concerning the composing, calculating method and characteristics of the fuel consumption for the intersections of urban arterial roads in Harbin, China.

The paper consists of following sections: Section 2 shows the data acquisition. Section 3 builds fuel consumption calculation model and its calibration; Section 4 analyses the characteristics of fuel consumption on intersections; Section 5 is the discussion; and the conclusions are drawn in Section 6.

Origin: 2 Huashan North Road, Nangang District, Harbin, China.

Destination: 228 Dongzhi Road, Daowai District, Harbin, China.

Road composing: 4 arterial roads including Huashan North Road, Xianfeng Road, Hongqi Avenue and Dongzhi Road.

Intersection types and composition: 10 intersections including 9 signalized intersections and a 3-way stop intersection.

Route distance: the whole route is 4370 meters long.

Pavement condition: the roadway pavements of the entire route are paved with asphalt concrete within 5 years and dry, and there are no significant ruts and other distresses.

The test route is designed and showed (

A gasoline car of 2003 Jetta CIF with 138,000 km and a 2011 Skoda Octavia with 72,490 km were used as the test vehicles and were both timely maintained. Meanwhile, there was a driver and a passenger in the car without any other loading. The relative technical parameters of the test vehicles are listed (

A software named VCDS ZHS 12.12.0 was designed by Ross-Tech LLC, which is a diagnostic and test system for VW-Audi group cars. Here, it was installed at the computer and connected with the test car using the given cable correctly before testing. The operation interface and the cable of the test system are showed (

From the test system, the data of engine velocity, engine load, injection timing, recording time and etc. were recorded as a Microsoft excel file.

Along the test route, the experiments were implemented during the non-peak hours, morning and evening rush hours on October 11^{th}, 13^{th}, 14^{th}, 16^{th}, 21^{st}, 22^{nd}, and 27^{th}, 2014 and July 1^{st}, and 2^{nd}, 2015. Consequently, 18 groups of experimental data were acquired by the test system.

_{a}), the deceleration _{d}), the idling _{i}) and the uniform velocity travelling _{u}).
_{a}, _{d}, _{i} and _{u} are the average acceleration time, the average deceleration time, the average idling time and the uniform velocity travelling time respectively (s); _{0} is the record time interval (s); _{a}, _{d}, _{i} and _{u} are the corresponding average _{a}, _{d}, _{i} and _{u} are the recorded segment counts.

From above analysis,

First, the fuel injection quantity for the ^{th} time interval is computed as _{e} is calculated as _{i} is the fuel injection quantity for the ^{th} time interval (ml); _{i} is the injection pulse width for the ^{th} record (ms); _{e} is the injection time interval (s); _{e} is the engine velocity (r/min); and

Second,

According to the collected data, average

The _{a} is the highest and it’s twice more of _{u} and four times more of _{d} and _{i}; _{i} is the lowest and with little gap of _{d}.

According to the above equations, the real

The average

The intersection influencing distance (IID) is defined as the area range that the vehicle operation is influenced by the existence of the intersection, which consists of two components, the former is the distance between the stop lines of the approach and exit of the intersection along the test route direction, it’s a constant; and the latter is the dilemma zone or the distance between the stop line of the approach and the location of the first stop in the intersection area, it’s a variable.

From the on-site measurement, the first part of the IIDs is 394 meters. However, the second part of the IIDs is varied from 658 meters to 1,200 meters and the average distance is 868 meters. Accordingly, the average is 1,262 meters occupying 28.9% of the whole distance of the test route.

The intersection operation time (IOT) is defined as the time duration of the test vehicle travelling during IID. The intersection operation time varies from 392 seconds to 1051 seconds and the average value is 576 seconds accounting for 68.5% of the total test time.

Here, a space-time diagram of the test vehicle from the collected data is showed (

In this space-time diagram of the test route, the main part of test time is made up of IOTs. Furthermore, key intersections can be identified as influencing the operation time and

From the data collected by the test system,

From the above statistical data, several findings can be discovered as the result of analysis.

For one thing,

For the other, the average _{a} is 109.05 ml accounting for 41.6% of the average _{i} is 96.47 ml accounting for 36.8% of the average _{u} is 28.05 ml accounting for 10.7% of the average

Meanwhile, different intersection

According to the data showed (

Commonly, the vehicular operating status (VOS) at an intersection can be reduced as three typical types (

For type (a), the vehicle gradually decelerates to stop behind the stop line or queue behind other vehicles when it faces the red light or the green light but cannot deal with all queuing vehicles of the approach. In general, the vehicle will have a process of deceleration-stop-acceleration to pass through the intersection.

For type (b), the vehicle passes the intersection with a uniform velocity approximately or with a process of slight deceleration-acceleration but without stop. This is one of the objectives of the signal control for all vehicles through the intersection and is favorable for saving

For type (c), the vehicle is unable to pass the intersection during the period of one signal cycle. So, the vehicle will suffer from several cycles of deceleration-stop-acceleration and the VOS is the most unstable as well. As a matter of course, it’s the worst control strategy and the most unfavorable for saving

According to data deduction, the space-velocity diagram for the test vehicle at a specific intersection can be drawn and showed following. Here, a local adjustment is done, but all roads lead to Rome; the distance is used instead of the time for horizontal axis because of more clearness for showing the vehicular operating status on different locations of the intersection.

It shows the space-velocity diagram of the test vehicle at the No. 2 intersection (

It shows the space-velocity diagram of the test vehicle at the No. 5 intersection (

It shows the space-velocity diagram of the test vehicle at the No. 6 intersection (

Causes of the high

As for the No. 6 intersection, there are 2 left-turn pocket lanes, 4 through lanes and 1 right-turn lane at the test route approach and 5 lanes at the exit; there are 2 left-turn pocket lanes, 2 through lanes and 1 right-turn lane at the approach and 3 lanes at test route exit. This intersection is very big actually, and the traffic demand is huge.
_{c} is the _{f} is the stop frequency.

The index of the stop frequency is of great importance for the computing

Stop frequency equals acceleration times. 2 diagrams (

From 2 diagrams (

Here, some relative improvement strategies will be present for reducing vehicle

First of all, traditional gasoline-powered vehicles should be replaced by the smaller and more efficient vehicles, i.e., hybrid, electric, biofuel, natural gas and etc. Meanwhile, STT for the engine can be promoted because it can reduce

Secondly, the intersections need to be broadened and well channelized for the approaches specially. Of course, it should be combined with the signal control design, especially with the phase design. The number of approach lanes is suggested to equal twice of the number of the road segment lanes; the classification of the lane function should be combined with the vehicle arrival rates; main traffic flow should be identified and should not change lanes frequently; the intersection area should be compressed as small as possible.

And then, optimized signal control strategies should be implemented for the arterial road intersections, for instance, multi-hour signal control schemes for the peak hours and non-peak hours, adaptive signal control method, and signal linear control technique; what’s more,

Finally, a dynamic velocity control method can be used for the intersection. Here, 2 variable message signs (VMSs) and 3 groups of sensors will be installed along every approach of the intersection. So, the VOS is expected to be adjusted from type (a) to type (b) or from type (c) to type (b). The optimal objective of

Finally we gave an outlook to the future research. To begin with, we will conduct much more experimental tests with kinds of vehicles including new energy vehicles, buses and trucks. Moreover,

The preliminary findings are as follows:

A test scheme of the

_{a}, _{d}, _{i}, and _{u}; these four parts are calculated by the recorded segment counts, time interval and corresponding _{a} is the highest and it’s almost twice of _{u} and four times more of _{d} and _{i}. Finally, the

The characteristics of _{a} was 109.05 ml accounting for 41.6% of the average _{i} was 96.47 ml accounting for 36.8% of the average

VOS and causes of the high

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Fuel consumption

_{a}

Acceleration

_{d}

Deceleration

_{i}

Idling

_{u}

Uniform velocity travelling

_{c}

_{a}

Average acceleration

_{d}

Average deceleration

_{i}

Average idling

_{u}

Average uniform velocity travelling

_{i}

Fuel injection quantity for the ^{th} time interval

_{i}

Injection pulse width for the ^{th} record

Number of cylinders

Parameter of the fuel injection quantity

_{a}

Average acceleration time

_{d}

Average deceleration time

_{i}

Average idling time

_{u}

Average uniform velocity travelling time

_{0}

Record time interval

Time variable

_{e}

Injection time interval

_{a}

Record acceleration segment count

_{d}

Record deceleration segment count

_{i}

Record idling segment count

_{u}

Record uniform velocity travelling segment count

_{e}

Engine velocity

Density of the gasoline

_{f}

Stop frequency for the vehicles