TRAFFIC SIGNAL CONTROL FOR REDUCING VEHICLE CARBON DIOXIDE EMISSIONS ON AN URBAN ROAD NETWORK

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1 TRAFFIC SIGNAL CONTROL FOR REDUCING VEHICLE CARBON DIOXIDE EMISSIONS ON AN URBAN ROAD NETWORK Toshhko Oda (1), Masao Kuwahara (2) and Satosh Nkura (3) (1) Panasonc System Solutons Company, Matsushta Electrc Industral Co., Ltd Tsunashma-hgash, Kohoku-ku, Yokohama , Japan Tel: Fax: E-mal: (2) The Insttute of Industral Scence, Unversty of Tokyo Komaba 4-6-1, Meguro-ku, Tokyo , Japan Tel: Fax: E-mal: (3) Kanagawa Prefectural Polce Headquarters 2-4 Kagan-dor, Naka-ku, Yokohama , Japan Tel: ext Fax: ext E-mal: ABSTRACT The level of carbon-doxde (CO 2 ) emssons, partcularly of those caused by motor traffc, has been ncreasng year after year n Japan. Ths paper proposes a traffc sgnal control method to reduce vehcle CO 2 emssons based on understandng ths background. Frst, a traffc flow smulaton model was appled to obtan wde area traffc flows, so that a smulator could be establshed to obtan an estmated fgure of CO 2 emssons generated by travelng vehcles whle consderng the state of each vehcle. An analyss of relatons between CO 2 emssons and both of delay tme and the number of stops at ntersectons was conducted wth the smulator, whch resulted n a proposal of a new traffc sgnal control method beng expected to help us reach our target to reduce CO 2 emssons. Then, smulaton experments were conducted on major arteral roads n Kawasak Cty to verfy the new method, resultng n a roughly 7 % reducton of CO 2 emssons from the levels of the exhaust gas reduced by the current traffc sgnal control method. As a result, the authors confrmed the new method to be effectve. INTRODUCTION Total carbon-doxde emssons are 1.24 bllon tons n Japan, whch s a 10.5 percent ncrease from 1990 levels of CO 2 emssons. Among others, 0.26 bllon tons, whch s equal to more than 20 percent of the total CO 2 emssons, s generated by transport ndustry, showng a 20.6% growth from 1

2 the 1990 levels. Motor traffc, n partcular, has caused more than 80 percent of the total emssons generated by the transport ndustry. Therefore, t s necessary to mprove vehcles and take varous measures to reduce CO 2 emssons for preventon of global warmng from now on. Though Traffc Control Centers (TCC) have managed urban traffc by takng many steps to smoothen traffc flows, t s now requred for the centers to take early measures for envronmental protecton to overcome such a severe condton. To acheve ths, t s necessary to estmate current levels of actual CO 2 emssons generated by motor traffc. Besdes, new traffc-related steps have to be planned and taken to evaluate estmated mpacts on reducng the emssons when they are ntroduced. Under the aforementoned background, ths paper descrbes a traffc sgnal control method, whch has shown ts good effectveness at the exstng TCC, and proposes a control method to reduce CO 2 emssons. Procedures for ths paper are as descrbed below. Frst of all, a CO 2 emsson-estmaton smulator s to be establshed to estmate CO 2 emssons generated by motor vehcles on arteral roadways under the traffc control. Ths system combnes a traffc flow smulaton model and the CO 2 emsson-estmaton model, to take nto account wde area traffc flows based on the traffc flow smulaton model and estmate a state of each travelng vehcle. In addton, such data are then appled to the emsson-estmaton model as an nput value for estmatng CO 2 emssons. At the same tme, we analyze a correlaton of the CO 2 emssons wth both of delay tme and the number of tmes a vehcle came to a halt at an ntersecton, whch were outputted from the traffc flow smulaton model. Then, based on the results, we suggest a traffc sgnal control method for reducng CO 2 emssons. Fnally, a smulaton test s to be conducted on major arteral roadways n Kawasak Cty to verfy the effectveness of the proposed method as to levels of reduced CO 2 emssons. SIMULATOR FOR ESTIMATING VEHICLE CO 2 EMISSIONS A CO 2 emsson-estmaton smulator, whch conssts of a traffc flow smulaton model and CO 2 emsson-estmaton model, has the followng features: Fgure 1 llustrates the smulator. (a) Beng able to apply a macroscopc model of traffc flow smulaton, (b) Beng able to estmate states of travelng vehcles, (c) Beng able to estmate CO 2 emssons every fve mnutes per lnk, and (d) Beng connectable wth multple traffc sgnal control methods. MACROSCOPIC TRAFFIC FLOW MODEL It s necessary to recreate wde area traffc flows snce CO 2 emssons generated by motor traffc are estmated based on data collected from major arteral roadways beng under traffc control. Hence, a macroscopc traffc flow smulaton model s appled to enable to transact traffc flows on wde area urban streets. We employed AVENUE [1] (an Advanced & Vsual Evaluator for road Networks n Urban areas) as a macroscopc smulaton model of traffc flows for ths research. AVENUE s packaged based on a tme scannng system, of whch mnmum unt of tme s one second, to recreate a traffc state for every scannng nterval. Also, t employs hybrd block densty system as a traffc flow smulaton model. The system dvdes each lane of a roadway nto 2

3 subsectons of meters long called Blocks, then t obtans a value of traffc volume to be shfted to other subsectons based on traffc flow characterstcs specfed for every block. The hybrd block densty system enables us to deal wth ndvdual vehcles separately, resultng n an ablty to utlze attrbutes of each vehcle classfcaton. Owng to ths, AVENUE has capabltes to package varous traffc measures or to combne a functon to allow drvers to select or change lanes, and s able to take nto account a locaton and a speed of each travelng vehcle for every scannng nterval due to ts functon to deal wth ndvdual vehcle behavors. Thus, t s able to obtan traveled path as well as travel tme of each vehcle. Oneyama et al. [3] have proposed a method, whch classfes a state of a travelng vehcle nto haltng, acceleratng, deceleratng, a fxed-speed travelng based on a lnear approxmaton of a travel locus, then estmates sectons the traveled n the classfed state. Ths paper employs ths method to determne the state of each travelng vehcle and to estmate sectons traveled n every state. Multple sgnal control methods Estmaton model of vehcle CO 2 emssons Vehcle CO 2 emssons Calculaton of Control Parameters Vehcle detector data Estmaton of states of travelng vehcles Sgnal tmngs Measurement of traffc flow Sgnal control Macroscopc traffc flow model Fgure 1 Vehcle CO 2 Emssons Estmaton Smulator VEHICLE CO 2 EMISSIONS EATIMATION MODEL Snce CO 2 emssons are to be estmated based on a state of each travelng vehcle n ths study, the authors ntroduce a CO 2 emsson-estmaton model to descrbe a relatonshp between CO 2 emssons and a condton of vehcle moton. The model proposed by Oguch et al. [2] s employed for ths paper for the estmaton. Ths model estmates CO 2 emssons as represented n the formula below: 3

4 2 E = 0.3K T K L K δ ( v v ) (1) c where, E: CO 2 emssons [kg], K c : coeffcent, T: travel tme n a lnk [sec], L: length of a lnk [m], and v k : velocty n tme k [m/sec]. Also, c c k k δ k would be 1 when a vehcle s acceleratng durng tme k, otherwse t would be 0. k 2 k 1 ESTIMATION OF VEHICLE CO 2 EMISSIONS CO 2 emssons generated by an ndvdual vehcle are to be derved by nputtng a travel speed, state and tme of every vehcle, whch are outputted through AVENUE, to the aforementoned emsson-estmaton model. The smulator aggregates the estmated fgures every fve mnutes per a lnk (sectons between sgnalzed ntersectons). It then sums up such fgures to obtan an estmated fgure of CO 2 emssons generated on an entre road network, to conduct an evaluaton n respect of ntroducng traffc measures. CONNECTING WITH TRAFFIC SIGNAL CONTROL METHODS Snce ths research apples to many dfferent sgnal control methods as wll de descrbed shortly, a functon to create control parameters (.e., cycle length, splts and offsets) requred to control traffc lghts s to be an external process toward AVENUE. Thus, data on sgnal tmng of each traffc sgnal s to be determned externally, then, gven to AVENUE. On the contrary, as smlar as the case under the current traffc control method, traffc flows are to be montored by nstallng vehcle detectors on AVENUE. Each vehcle detector measures traffc volume and tme occupancy, whch s to be provded for an external process to calculate the control parameters. Wth that, a smulated traffc sgnal control, ncludng external processes, s realzed. TRAFFIC SIGNAL CONTROL APPROACH It s necessary to determne approprate evaluaton ndexes as well as to create control parameters to optmze them for controllng traffc sgnals to reduce CO 2 emssons generated n a wde area under traffc control. There are two approaches to calculate the ndexes. One s to adopt the summaton of fgures of CO 2 emssons generated by ndvdual vehcles as the evaluaton ndex. Another s to educe a traffc ndex beng hghly nterrelated wth CO 2 emssons to employ t as an alternatve ndex to CO 2 emssons. The former approach employs equaton (1), yet t requres judgment whether each vehcle s n an acceleratng state or not, based on ts trajectory. Snce the approach would cover wde area traffc 4

5 flows causng a tremendous number of vehcles to be dealt wth, t takes consderable tme to process states of travelng vehcles. On the contrary, n the latter method, an attempt s made to fnd a traffc ndex whch s hghly nterrelated wth aggregated CO 2 emssons on all sgnal controlled routes, to determne optmal control parameters by applyng CO 2 emssons as an alternatve ndex. Though ths approach does not always create control parameters to mnmze the emssons, the approach s expected to calculate the control parameters to nearly mnmze them. Snce the traffc control center requres real tme sgnal control, the control parameters coverng a wde area have to be computed n a short tme. Takng the crcumstances on lmtatons on computaton resources nto consderaton, ths paper adopted the latter approach. CORRELATION ANALYSIS OF CO 2 EMMISIONS WITH TRAFFIC INDEX Evaluaton ndexes for traffc sgnal control nclude delay tme or the number of tmes a vehcle stopped at ntersectons. Ths secton analyzes a correlaton of CO 2 emssons wth the ndexes through carryng out a smulaton. Based on the emsson-estmaton smulator descrbed earler, the smulaton was conducted on major routes ncludng Sangyo-doro and natonal road Route 1 and 15 n southern area of Kawasak Cty. In the smulaton, tme seres traffc volume generated on the routes was gven to the smulator to output the levels of emssons generated on each lnk n every fve mnutes. Then, the results were aggregated to obtan a tme seres for each of the aforementoned routes. Delay tme and the number of stops at ntersectons were outputted as well to be totaled per lnk for every fve mnutes, as s the case wth the levels of emssons. Table 1 ndcates the analyss results of the smulaton. Data was aggregated per route. The table shows correlaton coeffcents of emssons wth delay tme and the number of stops n fve mnutes. Table 1 Correlaton Coeffcents of CO 2 Emssons and Traffc Indexes Routes Delay tme Number of stops Sangyo-doro Natonal Road Route Natonal Road Route The smulaton test results provded hgh correlaton coeffcents, ndcatng postve correlaton of both ndexes wth CO 2 emssons on every route. The obtaned coeffcents ndcated that the number of tmes a vehcle stopped had a partcularly hgh correlaton wth the levels of CO 2 emssons. Hence, t s expected that decreased traffc ndexes would cause to reduced CO 2 emssons. The followng subsecton s devoted to propose a sgnal control systems employng such ndexes as delay tme and the number of stops. PERFORMANCE INDEX FOR SIGNAL CONTROL A lnear sum of delay tme and the number of tmes a vehcle made a stop s appled n ths paper 5

6 as an evaluaton ndex for reducng CO 2 emssons. An evaluaton ndex road network s determned as the formula below: p of lnk n an urban p = α d + β s (2) where, d denotes delay tme n lnk, s denotes the number of stoppng n lnk, α and β denote weght coeffcents. Therefore, performance ndex P of the entre urban road network s represented as: P = p = + ( α d β s ) (3) OPTIMIZATION OF SIGNAL CONTROL BASED ON META-HEURISTICS In optmzng the control parameters, t s requred to compute an optmal value of the traffc ndex, for a combnaton of cycle length, splts and offsets n an entre wde area. In ths case, snce a functon to represent the ndex s not gven explctly, t s necessary to carry out a search to determne an optmal soluton. Ths paper employs meta-heurstcs, ncludng random search and genetc algorthm, for an effectve onlne and real tme search for a soluton. Addtonally, the number of varatons s to be estmated to enhance convergence of a soluton [4-5]. EVALATION OF SIGNAL CONTROL BASED ON ESTIMATION OF CO 2 EMISSIONS Sgnal tmng data are to be created based on the control parameters obtaned by the aforementoned procedures, to provde a control command for traffc sgnals n a traffc control area. On the contrary, when the CO 2 emsson-estmaton smulator s assumed to be a vrtual feld, t would be able to estmate CO 2 emssons by provdng the same command data for smulated traffc sgnals, whch enables evaluaton of the aforementoned sgnal control system. Besdes, the smulator would be able to estmate the levels of CO 2 emssons generated under the exstng control systems, e.g., pattern selecton control, by arrangng nput values to the smulator. In ths way, t would be possble to verfy an appled sgnal control method, as compared wth the current control method, regardng the levels of reduced CO 2 emssons. EXPERIMENTS EXPERIMENT PROCEDURE A smulaton test was conducted n a wde area, whch s now under control of the TCC, to verfy the effectveness of the sgnal control system proposed n ths paper. The test area covered the aforementoned three major arteral roads, whch were objects of the correlaton analyss. Traffc sgnals n the area are currently controlled va Kawasak Traffc Control Center, employng a pattern 6

7 selecton method to specfy the control parameters. Assumng that the CO 2 emsson-estmaton smulator was a vrtual feld, ths experment treated both of the proposed system and the pattern selecton method as external processes. In ths manner, each method created the control parameters based on vehcle detector data obtaned by the smulator, determned sgnal tmngs whle takng nto account nfluence from offset transton and others, then gave the control command data to the smulator. Also, smulated tme seres traffc was generated from 5:00 to 19:00 on weekdays to try these two dfferent sgnal control methods. Then, the estmated CO 2 emssons from the smulaton output under the two methods were compared. RESULTS OF EXPERIMENTS Fgure 2 and 3 show the test results. Fgure 2 ndcates a comparson between the levels of CO 2 emssons of the dfferent sgnal control systems. By dvdng the test hours nto three tme groups, Fgure 3 shows a tme-of-day comparson of CO 2 emssons. Every value n the both fgures s an estmated fgure for fve mnutes, and a correspondng value for a dstance of one klometer. 250 Natonal road Route 15 Natonal road Route 1 Sangyo-doro Vehcle CO2 emssons [kg] Current method Proposed method Fgure 2 Comparson of Estmated CO 2 Emssons between Two Methods 300 Vehcle CO2 emssons [kg] Current method Proposed method 0 5:00-10:00 10:00-15:00 15:00-19:00 Tme perod Fgure 3 Comparson of Estmated CO 2 Emssons n Each Tme Perod Based on these results, the authors confrmed the effectveness of the proposed method snce t was able to reduce the levels of CO 2 emssons by nearly seven percent as compared wth the 7

8 exstng one. Also, a route comparson revealed that the new method had a great mpact, partcularly on Route 1, wth the fgure of some twenty-four percent reducton n CO 2 emssons. On the contrary, concernng tme-of-day comparson of CO 2 emssons, each route showed dfferent patterns of fluctuaton n traffc flows dependng on tme of day, whch resulted n dfferent levels of CO 2 emssons. Yet, we revealed that the levels of CO 2 emssons were greatest durng a perod of tme from 10:00 to 15:00 as far as the entre three routes are concerned. Besdes, the hghest mprovement, whch was more than a ten percent reducton, was observed durng the same perod of tme. CONCLUSIONS The authors constructed a CO 2 emsson-estmaton smulator for ths research by utlzng a traffc flow smulaton model, AVENUE, to propose a sgnal control method ntended to reduce CO 2 emssons. Also, we made a correlaton analyss of CO 2 emssons wth delay tme and the number of stops based on the smulator. The results revealed a strong correlaton of CO 2 emssons wth the two factors. We then appled meta-heurstcs to establsh a system to control traffc sgnals through the optmzaton of control parameters whle employng delay tme and the number of stops as the ndexes. In addton, a wde area smulaton experment was conducted n the southern area of Kawasak Cty to confrm reducton n the levels of CO 2 emssons acheved by the proposed method. In future research, the authors plan on further mprovements for an onlne and real tme system to be packaged. The authors would lke to express a deep grattude to Dr. R. Horguch, -Transport Lab. Co., Ltd, who provded useful suggestons to ntroduce AVENUE for ths research; Mr. T. Otokta and Mr. H Fujwara, Panasonc Moble Communcatons Co., Ltd. Senda laboratory; as well as Mr. K. Iwaoka and Mr. T. Kawasak, Matsushta Electrc Industral Co., Ltd. for ther knd cooperaton n constructng the experment system and conductng the experments. REFFERENCES [1] R. Horguch, et at: A Development of a Traffc Smulator for Urban Road Networks: AVENUE, Proceedngs of Vehcle Navgaton & Informaton Systems, pp (1994) [2] T. Oguch, et at. : Carbon Doxde Emsson Model n Actual Urban Vehcular Traffc Condtons; Journal of the Japan Socety of Cvl Engneers, No. 695, Ⅳ-54, pp (2002) [3] M. Oneyama, et at: Estmaton Model of Vehcle Emsson Consderng Varaton of Runnng Speed, The 4th Conference of Eastern Asa Socety for Transportaton Studes(2001) [4] T. Oda, et at. : Heurstc Optmzaton of Traffc Sgnal Tmngs n TRANSYT Traffc Model, Proceedngs of 5thWorld Congress on Intellgent Transport Systems, CD-ROM (1998) [5] T. Oda, et at.: Meta-heurstcs Optmzaton of Traffc Sgnal Control and Sub-area Decomposton n Traffc Control Systems, Proceedngs of 10th World Congress on Intellgent Transport Systems, CD-ROM (2003) 8