EVALUATION OF AHS EFFECT ON MEAN SPEED BY STATIC METHOD

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1 EVALUATION OF AHS EFFECT ON MEAN SPEED BY STATIC METHOD Toshiyuki YOKOTA Senior Researcher, ITS Division, Public Works Research Institute, Ministry of Construction Asahi-1, Tsukuba-city, Ibaraki, Japan Phone: Fax: t-yokota@pwri.go.jp Satoshi UEDA, Shigeo MURATA ITS Division, Public Works Research Institute, Ministry of Construction The present paper describes the functionality and the expected effect of longitudinal control (acceleration and deceleration) in the Advanced Cruise-Assist Highway Systems (AHS) for the mixed traffic of AHS and non-ahs vehicles, and presents quantitative analysis of the effect of AHS on traffic efficiency improvement. The functionality and the expected effect of longitudinal control in AHS were first identified. The expected effect was then quantified by evaluating the improvement of traffic capacity with different settings of and other parameters. Finally, the improvement of travel speed due to the improvement of traffic capacity was quantitatively analyzed on a macroscopic scale by using a generalized QUALITATIVE ANALYSIS OF Q-V VARIATION IN AHS Traffic capacity, which is generally affected by regional traffic situations and geographical factors, is mainly determined by such road characteristics as uninterrupted sections (single lane, multilane), intersections, and interchanges. Although Q-V characteristics have conventionally been considered as unchanged, they can vary with such parameters as and the percentage of AHS vehicles if the use of AHS vehicles, which adjust their by the automatic control of acceleration and deceleration, becomes widespread. A conceptual model showing the relationship between and Q-V curves is presented in Figure 1. Here, the magnitude of (greater or smaller) is relative to 1

jp Satoshi UEDA, Shigeo MURATA ITS Division, Public Works Research Institute, Ministry of Construction The present paper describes the functionality and the expected effect of longitudinal control

2 non-ahs vehicles. In the case of AHS with a greater, as shown in the figure, the Q-V curve is scaled down (maximum Q-value is increased) as the percentage of AHS vehicles is increased. In AHS with a smaller, on the other hand, the Q-V curve is scaled up as the percentage of AHS vehicles is increased. The results of the analysis of qualitative characteristics of traffic based on an AHS traffic stream simulator are schematically shown in the Headway-QV model diagram. When the percentage of AHS vehicles in congested traffic is increased, traffic capacity changes significantly but speed and its variation remain nearly the same (O1 -> T1). When the percentage of AHS vehicles in free-stream traffic is increased, speed increases and its standard deviation decreases while traffic capacity remains the same (O2 -> T2). The above results indicate that speed tends to be maintained in a congested zone, while traffic demand tends to be maintained in a free-stream zone. Further analysis with varying settings is still needed for the qualitative evaluation of traffic. V T2 O2 Q-V including AHS vehicles that increase T2 Q-V including AHS vehicles that decrease T1 O1 T1 Q-V excluding AHS vehicles Q Figure 1. Headway - QV model RELATIONSHIP BETWEEN SPACING (HEADWAY) AND V (SPEED) IN AHS In the relationship between AHS vehicle spacing and speed, constant is presently assumed regardless of speed. S-V relationships for target of 0.5, 0.9 and 1.6 s are shown in Figure 2, in which minimum spacing, d, at rest is taken into 2

The results of the analysis of qualitative characteristics of traffic based on an AHS traffic stream simulator are schematically shown in the Headway-QV model diagram.

3 consideration. Here, traffic capacity Q for each is given by the reciprocal of the gradient of the corresponding S-V line. Spacing becomes a constant value of d if speed is below a certain level, above which a linear relationship is assumed between spacing and speed. Q Target 1.6 s Target 0.9s d Target 0.5s Legal speed V Figure 2. S-V figure considering minimum distance d IMPROVEMENT OF TRAFFIC CAPACITY BY AHS Traffic capacity in an uninterrupted multilane section was calculated for different of AHS vehicles (0.5, 0.9, and 1.6 s) and different percentages, d (%), of AHS vehicles. Each AHS vehicle is assumed to keep a target regardless of whether the preceding vehicle is AHS or non-ahs vehicle. The capacity of the mixed traffic of AHS and non-ahs vehicles is given by the following equation: Q d =3600 / ( h i d + h manual ( 1-d )) (1) Q d : capacity of the mixed traffic when the percentage of AHS vehicles is d (%) h i : target of AHS vehicle h manual : average of non-ahs vehicle d: Extension rate of AHS Shown in Table 1 are traffic capacities, as deduced from equation (1), when the percentage of AHS vehicles is 100%. Likewise, traffic capacity at an intersection when the percentage of AHS vehicles is d (%) was calculated based on the elementary flow rate at the intersection. Saturated flow rates when the percentage of AHS vehicles is 100% are shown in Table 2. 3

4 Table 1 Target of AHS and traffic capacity System Target Uninterrupted section (multiple lanes) Magnification Non-AHS vehicle - 2,200/h/lane - AHS-c ACC AHS-a Automatic traveling (platoon) 1.6 s 2,200/h/lane s 4,000/h/lane s 7,200/h/lane 3.3 Table 2 Target of AHS and traffic flow rate at junction System Target Uninterrupted section (multiple lanes) Magnification Non-AHS vehicle - 2,000/h/lane - AHS-c ACC AHS-a Automatic traveling (platoon) 1.6 s 2,200/h/lane s 4,000/h/lane s 7,200/h/lane 3.6 EVALUATION OF MACROSCOPIC EFFECT OF AHS ON TRAVEL-TIME REDUCTION FROM A GENERALIZED AGGREGATED Q-V EQUATION The improvement of travel speed due to the improvement of traffic capacity was quantitatively analyzed on a macroscopic scale by using equation (2), which is a generalized aggregated Q-V equation providing the average travel speed in an area from the total road length (converted to per-lane length) within the area and cumulative vehicle-kilometer. The generalized aggregated Q-V equation takes into consideration the effect of AHS on the increase of traffic volume and starting flow rate. The parameters used were estimated from the regional data of the road traffic census. R b V = a (2) DK c Here, V is speed, R is total road length, DK is cumulative vehicle-kilometer, and a, b, and c are parameters. 4

3 Table 2 Target of AHS and traffic flow rate at junction System Target Uninterrupted section (multiple lanes) Magnification Non-AHS vehicle - 2,000/h/lane - AHS-c ACC AHS-a Automatic traveling

5 For expressways, (a, b, c) = (33.6, 0.28, 0.19), multiple correlation coefficient = 0.83 For national highways, (a, b, c) = (24.6, 0.42, 0.43), multiple correlation coefficient = 0.93 Assuming that the increase of traffic volume by AHS is reflected in the total road length, estimation was made for three different target (1.6, 0.9, and 0.5 s) and for national highways and expressways as shown in Table 3. Traffic volume (cumulative vehicle-kilometer) was assumed to be unchanged from the present traffic demand. Table 3. Different cases of estimation National highway Expressway Target = 1.6s Case 1 Case 4 Target = 0.9s Case 2 Case 5 Target = 0.5s Case 3 Case 6 The results of the estimation are shown in Table 4. When the percentage of AHS vehicles is 100%, target of 1.6, 0.9, and 0.5 s result in increase in travel speed by factors of 1.03, 1.32, and 1.67, respectively, in the case of national highways, and 1.00, 1.18, and 1.39, respectively, in the case of expressways. Table-4 Evaluation of macroscopic effect on travel time reduction Type of road National Highway Expressway Case Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 AHS extension rate Target Headway 0% 20% 40% 60% 80% 100% 1.6 s 0.9 s 0.5 s 1.6 s 0.9 s 0.5 s

5 s) and for national highways and expressways as shown in Table 3. Traffic volume (cumulative vehicle-kilometer) was assumed to be unchanged from the present traffic demand. Table 3. Different cases of estimation National highway Expressway Target = 1.

6 Speed(km/h) AHS extension rate(%) Expressway h=0.5 Expressway h=0.9 Expressway h=1.6 National highway h=0.5 National highway h=0.9 National highway h=1.6 Figure 3. AHS mixture rate and the change of travel speed FINAL REMARKS In the present study, expected effect of the longitudinal control of AHS on traffic capacity improvement was quantitatively analyzed on a macroscopic scale by using a generalized aggregated Q-V equation. More detailed analysis of the functionality of AHS will be conducted in accordance with the development of AHS design. REFERENCES 1) Japan Road Association: Highway Capacity Manual, pp.19, September, ) Japan Road Association: The explanation and application of Road Structure Ordinance, pp , ) FHWA: Precursor Systems Analysis of Automated Highway Systems, Volume Four, Lateral and Longitudinal Control Analysis, pp.18, April,

AHS mixture rate and the change of travel speed FINAL REMARKS In the present study, expected effect of the longitudinal control of AHS on traffic capacity improvement was quantitatively analyzed on a

ESTIMATION OF FREE-FLOW SPEEDS FOR MULTILANE RURAL AND SUBURBAN HIGHWAYS

ESTIMATION OF FREE-FLOW SPEEDS FOR MULTILANE RURAL AND SUBURBAN HIGHWAYS Pin-Yi TSENG Professor Department of Traffic Science Central Police University 56, Shu Jen Road, Kwei Shan, Taoyuan, 33334, Taiwan