Signalized intersection with real-time adaptive control: on-field assessment of CO2 and pollutant emission reduction

Transportation Research Part D 9 (2004) 29–47 www.elsevier.com/locate/trd

Signalized intersection with real-time adaptive control: on-field assessment of CO2 and pollutant emission reduction Sophie Midenet *, Florence Boillot, Jean-Claude Pierrel
ee GRETIA Laboratoire G
enie des R
eseaux de Transport et Informatique Avanc
ee, INRETS Institut National de Recherche sur les Transports et leur S
ecurit
e, 2 avenue du G
en
eral Malleret-Joinville, 94114 Arcueil, France

Abstract The environmental costs linked to an isolated signalized intersection have been quantified in terms of CO2 emission, fuel consumption and standard pollutant emission. A multi-camera system automatically estimates vehicle idle time and stop rates per itinerary; each vehicle is affected a cost that depends on whether it stops at least once and on its idle time on red. Elementary costs are calibrated using real urban driving cycles and their corresponding emission profiles measured on test benches. These experimental data are used to calibrate average coefficients for catalyst converter gasoline vehicles, non-catalyst ones and diesel vehicles (passenger cars). An on-field experiment was performed during 8 months to evaluate the benefits of the CRONOS control strategy, compared to a time plan strategy with vehicle actuated ranges. The benefits observed have shown the potential of this adaptive real-time control strategy that uses video traffic sensors. Large benefits on stops and delay induce significant reduction in environmental damage: 4% reduction for CO2 emission in peak traffic whatever the type of engine, which corresponds to 14% reduction on the part of costs due to stops and delay. Such figures show that environmental costs can be reduced without giving up fluidity benefits.
2003 Elsevier Ltd. All rights reserved. Keywords: Signalized intersection; Real-time control strategy; Impact on pollutant emission; On-field assessments; Elemental emission model

1. Introduction This article deals with the environmental impact of traffic at signalized intersections and discusses the potential of advanced control strategy for reducing it. On-field experiments reported here were conducted in order to quantify the benefits provided by an adaptive real-time control *

Corresponding author. E-mail address: [email protected] (S. Midenet).

1361-9209/$ - see front matter
2003 Elsevier Ltd. All rights reserved. doi:10.1016/S1361-9209(03)00044-0

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strategy. Although we considered fuel consumption and standard pollutant emission (CO, HC and NOX ), we focused on CO2 emission. Technical improvements on engines cannot provide the same results for CO2 emission as those already reached for standard pollutants, yet CO2 emissions due to transport play a major role in the worrying problem of the greenhouse effect. Thus CO2 deserves special attention when evaluating ways of reducing environmental damage. Urban traffic control (UTC) techniques on urban areas represent one of these potential solutions. Urban traffic is characterized by highly changing speed profiles with frequent stops and starts at intersections. The costs of these stop-and-start sequences in environmental assessments are known to be high, which is why many studies have been carried out to estimate these costs (Biggs and Akcelik, 1986) and to evaluate the potential benefits of UTC techniques for reducing them. Several results have been published dealing with networks of intersections and improved control schemes of coordinated traffic signals (Alkadry and Khan, 2000). On-field experiments based on road traffic profile measurements indicate a clear potential for network-type itineraries but differ in their assessment (Abbott et al., 1995; Rakha et al., 2000). For instance Robertson et al. (1996) show that the MOVA strategy reduces CO2 emission compared to TRANSYT coordinated plans over a 6 km section of six signals in London; Andr
e et al. (1995) show that the extension of the local UTC system in Amiens reduces fuel consumption, whose magnitude varies considerably according to traffic conditions. The objective of this article is to assess traffic signal control strategy on a micro-scale around an isolated intersection. We have deliberately discarded the analysis of intersection offsets: we assume that our experimental area is limited to approximately 500 m long itineraries centered on an isolated intersection, with vehicles approaching and leaving at free cruising speed. Our goal is to assess through an on-field experiment whether or not an advanced traffic signal control strategy can modify traffic features in a way that leads to a significant reduction in emissions. Different models for assessing the impact of vehicle stops at traffic signals have been proposed in the literature, based on analytic approaches (Liao and Machemehl, 1998), traffic simulation tools like CORSIM (Hallmark et al., 2000), or on-field driving cycle recording (Rakha and Ding, 2001). In these studies emission and consumption estimates are derived from elemental models based on instantaneous emission data (Biggs and Akcelik, 1986). Advanced control strategy benefits on isolated intersections can be assessed on such a basis, as proposed by Hallmark et al. (2000). However it appears that very few on-field experiments have been performed using intersection scale assessment, like for instance the PRODYN strategy in Toulouse (Khoudour and Lesort, 1990). This article contributes three innovative aspects to this issue. The first one is the assessment of an adaptive real time control strategy. The CRONOS strategy (Boillot et al., 1992) continuously reacts to on-going local traffic conditions through video measurements such as queue-lengths, and it minimizes total delay. Its efficiency in reducing delay and stops compared to conventional control strategy has been demonstrated on site by Boillot et al. (2000). This makes CRONOS one of the most promising control strategies for minimizing environmental damage. This article investigates the range of savings that can be achieved using the CRONOS strategy. Second, this article describes a long-lasting daily on-field experiment on a signalized intersection. During an 8-month experimental period CRONOS and a baseline strategy controlled the intersection alternately. Traffic situations were analyzed continuously through video sensors that give detailed spatial and temporal measurements; these measurements were recorded for off-line assessment.

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Third, we propose an original method for calibration of an elemental model for emission estimates. Thanks to close collaboration with colleagues from the INRETS-LTE laboratory specialized in road emission models, an elemental model for emission estimates has been designed that reflects signal impacts on speed profiles for isolated intersections. An original calibration method based on relevant sequences in instantaneous emission profiles is described. 2. Experimental site and experimental system 2.1. Experimental site The experimental site is an isolated signalized intersection in the close suburbs of Paris in the Val-de-Marne Department (Fig. 1). It is made up of four double-lane inbound approaches and three double-lane outbound legs. The main road runs North–South (NS) and links Paris to the southern suburbs with high volumes of transit traffic at peak hours. The East–West (EW) road concerns local traffic with lower volumes. Eight road signal groups and seven pedestrian signal groups control the intersection. Four zones for left-turning vehicle storage are controlled by traffic signals in the inner area of the intersection. Right-turning vehicles have special links and do not cross the junction: they are not considered in our study. On the other hand U-turn volumes are high on the main road: an underground open highway along the NS road makes direct U-turns impossible for several hundred meters in both directions. We end up with eight crossing movements (straight and turning) from four incoming streams: three movements––or itineraries––from the North, two each from the South and from the West and a single movement from the East. The closest controlled intersections stand about 500 m away in the four directions. Our experimental area is centered on a 200 m radius round the intersection. This 200 m threshold corresponds to the distance covered by the video sensors. At that distance from the intersection, vehicles are assumed to be moving at cruising speed.

Fig. 1. The experimental site.

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Mobile radar sensors were used to measure incoming and outgoing speeds at this 200 m threshold during several measurement periods on successive days. Cruising speeds of four-wheel vehicles were recorded in order to constitute speed histograms with 10 km/h intervals. Measurements reveal high incoming and outgoing speeds with points higher than 100 km/h; distribution mode stands at the 60–70 km/h interval for the northern inbound approach. Speed histograms confirm the contrast between the NS road and the EW road in terms of both traffic speed and traffic volume. 2.2. Sensor traffic system The experimental area is covered by a set of video sensors that send traffic measurements to the INRETS laboratory several times per second. Eight cameras have been installed on-field on street lamps or dedicated poles near the intersection, roughly 9 m off the ground. The camera views are analyzed in real time using image processing techniques developed by Blosseville et al. (1989) and Aubert et al. (1996). The main advantage of using video sensors lies in the spatial covering of the traffic scene, leading to spatial measures that give robust and precise information concerning the traffic crossing the experimental area. We use several video-based traffic measures available every second: the queue length on inbound approaches, the number of stopped vehicles on each zone of the inner part of the intersection, and a flow indicator at the end of inbound approaches and the start of outbound legs. We speak about flow indicator rather than flow measure because of the non-optimal position of cameras that have been optimally positioned primarily for queue length or spatial occupancy. 2.3. Movement assignment and stop rate estimation module These traffic measures are collected continuously in the INRETS laboratory and feed a realtime dynamic intersection model (Sellam and Boulmakoul, 1994). We have developed a module that analyzes the second-by-second traffic measures given by the different cameras, and determines several cycle-by-cycle estimates: the volume of vehicles crossing the intersection per incoming stream and per movement, and the rate of vehicles which are stopped by signals at least once. The current system cannot follow vehicles at sensor level mainly because of the need for multi-cameras to cover the whole scene. This new module compiles movement flows across the intersection between successive green signals and assigns stop detection to these flows. Its basic principle is first to match stopped vehicles and outgoing flow indicators with each incoming stream, then to make assignment decisions after each complete signal cycle, and finally to assign a probability of non-stopped crossing to each reconstructed movement. The details of the method are given in Midenet et al. (1999).

3. An elemental model for signalized intersections This section presents the elemental model that we have designed in order to compare the environmental impact of two traffic control strategies on isolated intersections. This impact is estimated using average costs for fuel consumption and pollutant emission. The micro-scale of such

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assessments requires an elemental model with the following properties: it should primarily reflect the impact of traffic signals on speed profiles, in the particular context of an isolated intersection, taking advantage of spatial traffic measurements since individual speed profiles are not available. 3.1. The model In the neighborhood of a signalized intersection, two classes of speed profiles must be considered depending on whether the signals do or do not set up a stop-and-start sequence (Fig. 2). A stopped vehicle induces an elementary cost that corresponds to a stop-and-start sequence: cruisethen-deceleration cost, idle-time cost and acceleration-then-cruise cost. The cost induced by a non-stopped vehicle can be approximated just taking cruising speed costs. For vehicle h crossing the area the cost ce is:
cCruDec ðsin Þ þ cIdle þ cAccCru ðsout Þ if h stops at least once ð1Þ ce ðhÞ ¼ if h does not stop cCrus ðsin Þ þ cCrus ðsout Þ where sin is the cruising speed entering the inbound approach, sout is the cruising speed exiting from the outbound leg, cCruDec is the partial cost over the inbound approach when the vehicle stops, cAccCru is the partial cost over the outbound leg when the vehicle starts, cIdle is the partial cost due to idling on red signal, and cCrus is the partial cost due to cruising over the inbound approach or the outbound leg. For simplification purposes we consider the inbound approaches and the outbound legs as being equal in length L; we use L ¼ 215 m. For simplification purposes too, the movement inside the inner zone of the intersection is ignored. Successive stops are usual for turning movements when two or more red signals are encountered, but we do not consider the short moves between stops. When vehicles do not stop, an absence of deceleration is not realistic especially for turning movements, but its influence is assumed to be negligible compared to the influence of stops.

Fig. 2. Two classes of speed profiles.

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As explained in Section 2 the traffic measurement system classifies input stream flows between stopped and non-stopped profiles and compiles idle time, but does not restitute speed profiles. That is why we have turned to traffic profile databases with real urban speed profiles. For each speed level we select real sequences of stop, start or cruising profiles, match their corresponding emission profiles and compile average individual emission costs. This calibration phase has been done thanks to close collaboration with colleagues from INRETS-LTE who have been investigating instantaneous emission models for several years. The calibration phase is performed as follows. 3.2. Calibration using driving cycles and instantaneous emission measures The emission estimates rely on speed profile databases and their corresponding emission records, collected during different research projects over the past few years by INRETS-LTE and partners. These driving cycle databases, known as DRIVE-MODEM, MODEM-IM and MODEM-HYZEM cycles, are described in Andr
e et al. (1994) and Andr
e and Pronello (1997). The driving cycles mostly concern urban traffic, with a few non-urban cycles for high speed data; they are based on more than 1600 h of driving (Andr
e et al., 1994). Emission profiles have been recorded on test benches based on these driving cycles with vehicles operating under hot stabilized conditions. Our elemental model is based on more than 300 hours of instantaneous emissions, recorded for CO2 and regulated pollutant CO, HC and NOX (Lacour et al., 2000a). Different types of vehicles have been chosen to represent the European vehicle fleet. Three categories of passenger car have been defined with several standard European models following different European norms in each case (Jost et al., 1994): vehicles using gasoline with a catalyst converter, vehicles using gasoline without a catalyst converter, and diesel passenger cars. These experimental databases enable us to determine emission coefficients for different environmental costs (CO2 , CO, HC and NOX emission, fuel consumption) and different vehicle categories. Averaged unit emission coefficients are compiled alternately one at a time for each vehicle category. 3.3. Determination of emission coefficients for 10 km/h ranges of speed Coefficients for 10 km/h ranges of stabilized speed between 0 and 110 km/h have been determined using a sequential model with the following method (please refer to Lacour et al. (2000b) for a detailed description). A procedure with predefined criteria has been designed to automatically isolate three types of short-lasting sections in the data base profiles: deceleration from stabilized speed until complete stop, acceleration from complete stop until stabilized speed, and stabilized speed sequences and idle sequences. A method for matching selected sections with corresponding emission sections has been developed. Each emission section is given a shifting delay using a correlation maximization procedure, in such a way that late effects of kinematics parameters can then be taken into account. Integrated emission coefficients over the entire distance are compiled by integrating the instantaneous emission profiles. Deceleration and acceleration costs are completed when necessary by stabilized speed costs in such a way that the total section covered is length L. At this stage we get a sample of individual costs cCruDec ðsÞ, cAccCru ðsÞ and cCrus ðsÞ corresponding to sections of length L for various cruising speeds s. We end up with a reasonable number of

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individual costs per speed range, usually around 10, a bit less for coefficients corresponding to the highest level of stabilized speed. A mean value for each speed range is computed on the basis of the set of individual costs. Concerning the idle-time coefficients, the same kind of procedure is followed––except for the shifting of profiles––to estimate 1-s cost samples and averaged idle-time coefficients. 3.4. Application to the experimental intersection We use the speed distribution histograms and compute weighted coefficients for each leg that reflect its speed distribution: costs related to speed ranges are weighted according to its part in the speed distribution of the leg. We end up with three weighted coefficients per leg l for each environmental damage and each vehicle category: cCruDec ðlÞ, cAccCru ðlÞ and cCrus ððlÞ. We then consider itinerary representation: for each itinerary j connecting the inbound approach inðjÞ with the outbound leg outðjÞ we define elementary costs per itinerary such that: cstop ðjÞ ¼ cCruDec ðinðjÞÞ þ cAccCru ðoutðjÞÞ nostop

c

ðjÞ ¼ cCrus ðinðjÞÞ þ cCrus ðoutðjÞÞ

For vehicle h following itinerary j the cost ce is:
stop c ðjÞ þ cidle td if h stops at least once along j ce ðhÞ ¼ if h does not stop along j cnostop ðjÞ

ð2Þ ð3Þ

ð4Þ

where td is the delay 1 or idle time expressed in seconds, cidle is the mean cost of one second idle time, cstop ðjÞ is the elementary cost of a stopped vehicle on itinerary j, and cnostop ðjÞ is the elementary cost of a non-stopped vehicle on itinerary j. Let dcstop , the overcost induced by a stop, be defined as: ocstop ðjÞ ¼ cstop ðjÞ
cnostop ðjÞ

ð5Þ

Then ce ðhÞ ¼ cnostop ðjÞ þ bstop ðocstop ðjÞ þ cidle td Þ where bstop ¼




1 0

ð6Þ

if h stops at least once if h does not stop:

3.5. Final model expression The total cost during time period s becomes: X C total ¼ ðcnostop ðjÞNj þ ocstop ðjÞNjstopped Þ þ cidle Td j

1

In the scope of the paper delay is always synonymous with idle time.

ð7Þ

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where j is the itinerary across the intersection, Nj is the total number of vehicles using itinerary j during period s, Njstopped is the number of stopped vehicles using itinerary j during period s, and Td is the total delay during period s, and average unit cost: C total N

ð8Þ

Td ðin s veh
1 Þ N

ð9Þ

Njstopped ðin %Þ Rj ¼ Nj

ð10Þ

C u ¼ C uStop þ C uNostop

ð11Þ

Cu ¼

P where N is the total number of vehicles during period s : N ¼ j Nj . One part of this cost results from stops and delay; it is denoted C uStop . The other part denoted uNostop corresponds to stable-speed passing-through crossing. Under our assumptions the latter C does not depend on the traffic signal control strategy and corresponds to an irreducible cost. If we define the average unit delay Du and the rate of stopped vehicles Rj by: Du ¼

then

where C uStop ¼

X ðocstop ðjÞRj Þ þ cidle Du j

C uNostop ¼

X j

cnostop ðjÞ

Nj N

ð12Þ

 ð13Þ

3.6. Comments on coefficient values We end up with three classes of coefficients for each type of vehicle and each environmental damage: cnostop ðjÞ and ocstop ðjÞ for the eight itineraries j, and cidle . Most coefficients vary significantly over the eight itineraries: ocstop ðjÞ for CO2 varies up to 55% (relative difference between minimum and maximum). This reflects the sensitivity to speed for CO2 emission whatever the vehicle category. The overcost due to stop coefficients can be compared to idle-time coefficients. Table 1 depicts the ratio meanj ocstop ðjÞ=cidle . It appears that stopping a vehicle is equivalent to 34 s of idle time regarding CO2 emission for diesel engines and 24 s for gasoline engines with catalyst converters. On the other hand the latter are much more sensitive to stop––and more so than diesel ones–– concerning pollutant emissions: one stop equals 247 s of idle time for CO emission. The influence of stops and delay can be analyzed independently by looking at the ratios ocstop =cnostop and cidle =cnostop . Categories for which these two ratios are high are expected to be

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Table 1 Comparative costs of stop and idle time Environmental criteria

CO2

Cons.

CO

HC

NOX

Vehicle category Gasoline with catalyst Gasoline, no catalyst Diesel

24 28 34

26 30 34

247 45 22

57 14 17

122 176 28

Stopping a vehicle across the experimental area costs the same as keeping it idling during the following periods (in seconds).

sensitive to traffic signal control strategy optimization. The three classes of vehicles behave almost the same for CO2 and fuel consumption regarding these criteria. On the other hand, when looking at the three pollutant ratios, situations differ considerably from one category of vehicles to the other. Catalyzed gasoline vehicles show a high sensitivity to stop, which can be related to the catalyst behavior during acceleration. Diesel vehicles are comparatively more sensitive than others to delay. Let us mention the case of HC emission for diesel vehicles, which turn out to be the most sensitive category for stop as well as delay.

4. The on-field experiment 4.1. Control strategy comparison The assessment of the CRONOS control strategy took place under favorable conditions: the ability to actually control the traffic signals from our laboratory, the facilities of complete recording during experiments for precise off-line assessments, and the long-time availability of the experimental procedure in terms of technical, administrative and legal facilities. An experimental phase was set up where the CRONOS strategy controlled the connected intersection alternately with a comparative strategy as baseline conditions. The CRONOS strategy is an adaptive real-time control strategy; its algorithm optimizes traffic signal states according to queue lengths on approaches and occupancy rates on internal sections measured every second by the video sensors. The CRONOS algorithm (Boillot et al., 1992) is made up of a forecasting module that predicts the arrivals on each approach over the following minute, a modeling part to simulate the traffic scene over this horizon considering many traffic signal state sequences, and an optimization method designed to select the best signal state sequence to minimize total delay. The alternative strategy, referenced here as the baseline strategy, has been designed by traffic engineers from the local road authority; it was updated just before the experiment began to adapt parameters to recent changes in traffic volume. This strategy is used for controlling the intersection as an isolated one, i.e. independently from its neighboring junctions. It consists of a local time plan with vehicle actuated ranges on each approach. The variation induced by this micro-regulation mechanism makes the cycle duration vary up to 50% above its minimum value. The main features of both strategies are summarized in Table 2.

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Table 2 Comparative features of CRONOS and baseline control strategies Baseline strategy

CRONOS strategy

Type of control

Isolated Time-plan based

Isolated Real-time: no cycle, no stage

Adaptativity to traffic

Slightly adaptive Vehicle actuated

Highly adaptive Permanent optimization

Traffic sensors

Magnetic loops on entries

Video sensors on entries, exits and inner sections

Design

Local traffic engineers

INRETS

4.2. Experimental procedure The experimental procedure was conducted in order to assess the two strategies on regular traffic situations occurring on the intersection site. For practical reasons we restricted the traffic situations we studied to weekday periods from 8.00 to 18.30; daytime covers peak periods down to mid-afternoon low traffic periods. The goal was to collect a large set of traffic scenes under homogeneous and comparable conditions for both strategies. These conditions refer to traffic volumes as well as external parameters that could affect traffic flow or the experimental monitoring system, such as meteorological conditions. A single strategy controlled the intersection during time windows that were long enough to get stabilized traffic situations: we used half-day four-hour time windows. These four-hour windows were dedicated alternately to different strategies to ensure that traffic and external conditions were combined during the whole of the experiment: one strategy was randomly selected and applied during a given time window. The experiment lasted 8 months from July 1998 to February 1999. It resulted in a large set of half-day windows for each strategy with the whole range of conditions: day of the week, morning or afternoon period in the daytime, weather, luminosity, sensor system functioning. On-field application of a control strategy always went hand-by-hand with the complete recording of the experimental conditions; automatically through the experimental system (video tapes, magnetic loops and video-based traffic measures, traffic signal states) with additional information collected manually in the log book (general conditions for half-day time windows concerning the weather, traffic, incident or system functioning characteristics). 4.3. The database We adopted hourly periods of integration in order to define the observation samples: each halfday record was split into four fixed hourly windows, from [8.00, 9.00] to [11.00, 12.00] in the morning and from [14.30, 15.30] to [17.30, 18.30] in the afternoon. Hourly windows seemed short enough to ensure homogeneous conditions and large enough to guarantee strategy-independent volumes of traffic. Thus traffic demand N and delay Td are integrated over one-hour time periods (cf. parameter s in Eq. (7) and further). Samples relating to unexpected or unusual traffic scenes were discarded before statistical analysis. For example, congested traffic situations with at least one congested outbound leg hardly

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ever occur on our site: we obtained only three half-day records of congestion. Non-typical traffic situations obviously should be evaluated, especially congestion as far as pollution and fuel consumption are concerned, but neither the assessment method, the emission estimates nor the calibration can be applied under such conditions. Samples were also discarded where the observation system was not performing under standard nominal conditions, because the assessment method relies on the traffic measures given by the observation system. The video sensors had been used intensively for several years in our laboratory and had already been assessed individually (Aubert et al., 1996). Their functioning is presumed to be nominal in any meteorological or light conditions provided the video scene is captured by the cameras. Some blind camera problems occurred several times during the experimental period: 2 a Christmas tree temporary installed along the intersection for a few days masked the traffic, several minutes at sunset blinded one camera. This led us to discard the corresponding hourly observations. The last step consisted in building different subsets of observations that could be compared in terms of traffic situations. We used total hourly traffic volumes N to identify the main classes of traffic situations. Thus we assumed that similar hourly volumes ensure homogeneous traffic situations––in terms of stream and itinerary distribution for instance, and we assumed that hourly volumes are independent of the control strategy. We defined four classes of traffic conditions by considering four ranges in total hourly volumes: peak-hour traffic, dense traffic, fluid traffic and low traffic (see Table 3). 4.4. Assessment criteria and control strategy comparison Based on traffic measurement records and using the movement-assignment and stop-rate estimation module, we calculated for each hourly sample the average environmental costs with the part that results from stops and delay. These costs are averaged over the set of samples from the same category in terms of traffic situation and operating control strategy. For both control strategies we obtained average unit costs C u and corresponding partial costs C uNostop and C uStop expressed in g veh
1 . In the same way fluidity related criteria were averaged over the different categories of hourly samples. For both strategies we ended up with the following fluidity costs: • the average unit delay or idle time Du expressed in s veh
1 • the average stop rate in percentage P stopped Nj stopped N j R¼ ¼ : N N

2 It is important to stress that temporary misfunctioning of one sensor did not prevent the global video-based strategy from working properly. We did not interrupt on-field CRONOS control when a blind camera problem occurred: we observed that the traffic continued to flow normally. This needs to be assessed and quantified but is beyond the scope of this article.

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Table 3 Database made up of hourly samples Traffic situation

Peak hours

Dense traffic

Fluid traffic

Low traffic

2100–2600

1600–2100

900–1600

Demand range 2600–3300 (veh h
1 ) Total samples 53 Hourly distribution (Cronos/Baseline) [8.00, 9.00] 9/8 [9.00, 10.00] 2/3 [10.00, 11.00] 0/0 [11.00, 12.00] 0/0 [14.30, 15.30] 0/0 [15.30, 16.30] 0/1 [16.30, 17.30] 3/5 [17.30, 18.30] 10/12

74

129

34

1/0 13/15 2/1 1/3 3/1 7/9 9/6 2/1

0/0 3/3 16/20 12/19 15/19 13/9 0/0 0/0

0/0 0/1 4/4 5/4 2/6 2/6 0/0 0/0

Total

38/36

59/70

13/21

24/29

The comparison of each cost C for the CRONOS strategy and the baseline strategy is given by the relative benefits of the former over the latter, as given by the following equation: C baseline
C cronos ð14Þ c¼ C baseline where C is the mean cost averaged over the relevant set of samples. c > 0 indicates a decrease in cost. All values of benefits given in Section 5 are significant for at least 95%. 5. Experimental results 5.1. Baseline conditions Fig. 3 shows the average environmental costs per unit C u under the baseline control strategy. They remain almost constant through traffic situations despite a slight decrease when traffic demand decreases. These figures illustrate some well-known features: the non-catalyst gasoline category shows a little less CO2 pollution and more fuel consumption; the catalyst converter improved tremendously the engineÕs performance as to CO, HC and NOX emissions; diesel vehicles emit five time less CO than catalyst gasoline ones, but twice as much NOX . Table 4 shows the fluidity costs using the baseline control strategy. In off-peak conditions the average delay per vehicle does not depend on the traffic situation; in peak hours it increases significantly. The stop rate culminates in peak period too but decreases slightly in low traffic period; its average value is close to 75%. Stops and delay have a strong influence on the environmental costs, as depicted in Fig. 4. Whatever the type of vehicle and the traffic condition, at least 25% of the environmental costs are directly induced by stops and delay. Considering CO2 emission and fuel consumption, the stops-and-delay part remains quite stable over the category of vehicle and the traffic condition, and constitutes between 25% and 30% of

S. Midenet et al. / Transportation Research Part D 9 (2004) 29–47 Fuel consumption in g/veh

CO2 emission in kg/veh

35 30 25 20 15 10 5 0

0.10 0.08 0.06 0.04 0.02 0.00 peak

dense

gas. catalyst

fluid

low

gas. no catalyst

41

peak

diesel

dense

gas. catalyst

fluid

gas. no catalyst

low diesel

HC emission in g/veh

CO emission in g/veh 12 10 8 6 4 2 0

1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

peak gas. catalyst

dense

fluid

low

gas. no catalyst

peak

diesel

dense

gas. catalyst

fluid

gas. no catalyst

low diesel

NOx emission in g/veh 1.2 1.0 0.8 0.6 0.4 0.2 0.0 peak gas. catalyst

dense

fluid

gas. no catalyst

low diesel

Fig. 3. Average environmental unit costs C u using the baseline control strategy. Table 4 Fluidity costs for the baseline control strategy Traffic situation

Peak

Dense

Fluid

Low

Delay Du (s veh
1 ) Stop rate R (%)

21.0 75.2

19.2 74.5

18.7 74.6

19.4 72.1

total damage. In both cases, catalyst vehicles show slightly higher percentages––and therefore higher stakes––than diesel ones. Figures are more heterogeneous for standard pollutants, but the potential reduction is proportionally greater. More than 36% of CO and HC costs are induced by stops and delay, up to 80% for the diesel category. Catalyst vehicles constitute the most promising category for NOX reduction with around 42% of total costs due to stops and delay. 5.2. The CRONOS strategy induced benefits Fig. 5 shows the relative benefits c of the CRONOS strategy compared to the baseline strategy, according to Eq. (14). The CRONOS strategy leads to environmental benefits for all the criteria studied: CO2 emission, fuel consumption and CO, HC, NOX pollutant emission. The observed benefits are

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Fig. 4. Relative part of stops and delay C uStop =C u using the baseline control strategy.

statistically significant in all cases for each type of vehicle and each traffic situation. Most of the figures lie between 3% and 6%. The highest benefit exceeds 14% for HC emission of diesel vehicles during low traffic situations; the lowest is 2.5% (NOX , non-catalyst, fluid traffic). As expected the benefits given by the new strategy are closely related to the relative part induced by stops and delay in environmental costs observed using the baseline strategy. When comparing the three types of engines under the same conditions, it is confirmed that in most cases the higher the part induced by stops and delay using the baseline strategy, the higher the benefit induced by the CRONOS strategy. The CRONOS strategy reduces CO2 emission and fuel consumption slightly more for catalyst gasoline vehicles (5% for peak periods) than for diesel ones (4.5%). Conversely, the CO and HC emissions of diesel vehicles are considerably improved: 10% and 14.2% in peak period. Nevertheless the link between the part due to stops and delay and the relative benefits is not systematic: benefits for NOX emission are higher than expected for diesel vehicles compared to non-catalyst results. Furthermore these benefits reveal a marked influence of the traffic situation. The CRONOS strategy leads to even better savings under both extreme traffic situations, peak

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Fig. 5. Benefits c on costs C u using the CRONOS control strategy.

period and low traffic. This trend is clear whatever the criteria and the reason must lie in the efficiency of the control strategy. The benefits on stops and delay of the CRONOS strategy are very high. The figures reported in Table 5 are from Boillot et al. (2000). It should be stressed that benefits are high whatever the traffic situation, and that the CRONOS strategy is primarily efficient for delay minimization but achieves 8.8% benefit on average in the stop rate. For both stops and delay, the benefits are higher for peak periods than for medium traffic situations, this making peak periods good candidates for higher benefits in environmental costs. Moreover the criteria with high sensitivity to delay over stops are expected to reach high benefits on low traffic situations too. The benefits on the partial costs induced by stops and delay are given in Fig. 6. The CRONOS strategy has proved to be extremely efficient: the benefits on C uStop usually lie between 12% and Table 5 CRONOS benefits versus baseline control strategy on fluidity costs Traffic situation

Peak

Dense

Fluid

Low

Benefits on delay (%) Benefits on stop rate (%)

19.3 12.2

16.0 8.2

17.5 6.5

27.5 8.1

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Fig. 6. Benefits c on partial costs C uStop using the CRONOS control strategy.

18%, the minimum being 7.9% (CO for catalyst vehicles in fluid traffic) and the maximum 19.4% (HC for non-catalyst vehicles). CRONOS strategy efficiency is substantial for CO2 emission and fuel consumption, more than 14% on average. The relative benefits remain homogeneous over vehicle categories with a slight advantage for catalyst vehicles and disadvantage for diesel ones, respectively the most and least sensitive to delay versus stops. Peak periods and low traffic ones seem almost equally favorable. For standard pollutants, the benefits largely depend on vehicle category. Catalyst vehicles represent the least favored vehicle category because of its high sensitivity to stops over delay. Conversely diesel vehicles reach their highest results, almost 19% for HC in low traffic situations. 5.3. Comments and analysis An adaptive real-time control strategy can lead to a limited but significant reduction in CO2 emission over 430 m itineraries crossing an isolated signalized intersection. Table 6 shows some global amounts of savings per hour using the CRONOS strategy. The figures have been compiled from total costs C total summed over hourly intervals. We used modal distribution corresponding to the French fleet in the mid 1990s: roughly 35% of diesel vehicles, 25% of catalyzed gasoline

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Table 6 Rough estimates of global savings per hour Global savings per hour

Global CO2 Emission saved

Global fuel consumption saved Global delay saved

In 1 h of peak traffic In 1 h of fluid traffic

8 kg [out of 222 kg] 4 kg [out of 145 kg]

3 kg [78 kg] 1.5 kg [51 kg]

3h050 [16h250 ] 1h400 [9h400 ]

vehicles and 40% of non-catalyzed ones. Between 4 and 8 kg of CO2 can be saved each hour on the intersection, savings over an entire day reach several dozen kilograms. It is interesting to note that the rough estimates of our results are comparable with other figures that can be found in the literature for comparable scales, as reported in Hallmark et al. (2000). Although the CRONOS strategy is designed to minimize global delay, it also produces significant benefits on stop rates. This constitutes an important result as it shows that it is possible to save on environmental costs while improving fluidity. Detailed results show the impact of reducing stops and delay on the inner part of the intersection in the global assessments and highlight the interest of using video-type traffic sensors for signal control since they can handle complex traffic zones.

6. Conclusion The benefits observed have shown the potential of an adaptive real-time control strategy which uses advanced traffic sensors. High benefits on stops and delay achieved with the CRONOS control strategy lead to significant reduction in environmental damage on isolated intersection: 4% reduction for CO2 emission in peak traffic, corresponding to 14% reduction in the part of costs due to stops and delay. Such figures show that environmental costs can be reduced without affecting traffic fluidity. The benefits of such control strategies when used on a larger scale for several networked intersections still have to be quantified. Greenhouse gas cannot be reduced using only traffic control management solutions. However we have shown that for a given volume of traffic and a given infrastructure there is still room to reduce environmental costs using advanced traffic control methods, even at a micro-scale level. Our experimental method ensures that the two control strategies have been compared under identical levels of traffic: by randomly switching the control strategy from one four-hour period to another, we ensure that no effects are induced on the traffic demand since the drivers cannot predict the improvements. The results of our assessment experiment give strong evidence in favor of real-time adaptive control strategy with advanced spatial traffic sensors. When dealing with UTC assessment it is essential to investigate all kinds of impact; the benefits on fluidity and environment being analyzed, we are now developing a safety-oriented assessment model.

Acknowledgements The authors would like to express their gratitude to their colleagues from INRETS-LTE Michel Andr
e, Robert Joumard and St
ephanie Lacour, for their close collaboration in the design and

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calibration of the emission estimate model. The authors are grateful to ADEME, the French Agency for Environment and Energy Management, for their financial support, and to the Conseil G
en
eral and the Prefecture-DDE in the Val-de-Marne, for allowing us to control the intersection and for their help during the experiments.

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