Evaluation of Network Traffic Performance Measures

Transcription

1 Transportation Research Record valation of Network Traffic Performance Measres by Use of Compter Simlation Models WLLAM D. BRG AND CHUL-UNG DO The relation between traffic-signal-timing parameters and selected traffic performance measres of effectiveness (MOs) was investigated by compter simlation of peak-hor flow conditions on an rban arterial in Madison, Wisconsin. The MO s inclded delay, stops, fel consmption, and exhast emissions. A variety of signal-timing plans were generated by sing time-space diagram methods end the TRANSYT signal timing optimization model. Two compter simlation models, TRANSYT and NTSM, were then sed to develop traffic performance data for evalation prposes. The reslts of the stdy showed that tho signal-timing parameters that had the most significant effect on the MOs were cycle length and the K-factor in the TRANSYT performance index. Speed of progression was highly correlated with nmber of stops: A higher vale yiolded a lower nmber of stops. Priority policy and split method did not show o significant impact on tho MOs. All MOs can be improved when optimized timing plans arosed nstead of those developed by time-space diagram methods. n a comparison of tha TRANSYT and NTSM simlation models, the NTSM model prodced higher vales for the MOs nder a given signal-timing plan. n a comparison of MOs, nmber of stops and NOx showed a close correlation whereas delay appeared to be a strong srrogate for the other principal MOs. Traffic behavior variables sch as delay and stops have traditionally been sed as indicators of the level of performance of a variety of traffic operations and control strategies. However, since the oil embargo of 1973, atomobile fel consmption has received increasing attention as an additional important performance measre. Recent research dealing with fel consmption as a measre of effectiveness (MO) has prodced inconsistent findings with respect to its relation to certain traffic signal timing parameters, as well as other MOs. For example, in 1975 Baer C!l developed a model of fel consmption at signalized intersections based on Webster's eqation for intersection delay. Testing of the model revealed that the cycle length at which fel consmption is minimized apparently is significantly greater than the cycle length at which delay is minimized (see Figre 1). n a sbseqent investigation, Corage and Parapar <ll fond similar reslts for a network of 26 signalized intersections in Gainesville, Florida. By sing estimates of delay and nmber of stops from the TRANSYT compter model () and applying Claffey' s (il fel-consmpt ion coefficients for a composite vehicle on level Figre 1. Delay and energy consmption verss cycle length for intersection with total critical flow of 14 vehicles/h. 6! a;!l! l!o x :.c: "' c >!1 14 Deloy,., 4! nerqy c ! ! ) Cycle Length (Seconds)! "' Q "' c a "' c a; ::> LL grond with an approach speed of 3 miles/h, Corage and Parapar fond that minimm delay wold be achieved at a 9-s cycle length and fel consmption wold be minimized at a 14-s cycle length (see Figre 2). Dissimilar findings were reported in a 1979 stdy by Cohen and ler (), who sed the NTSM traffic flow simlation model (_ _) to evalate the relation among fel consmption, vehicle emissions, delay, and signal cycle length for an isolated intersection with a two-phase, fixed-time signal. Cohen and ler fond that the cycle length at which minimm delay occrs is the same as that at which minimm fel consmption occrs (see Figre 3) Figre 2. Fel consmption and delay verss cycle length for signal system of Gainesville, Florida, central bsiness district. 4 i 3!O K "! &. > ", "i c " JOO 2 =8)'. (K O) Cy le Le ngf h ( Seconds) Figre 3. Fel consmption and delay verss cycle length for isolated intersection with critical flows of 18 vehicles/h (no left trn, 1 percent right trn and 4 vehicles/h (no left trn, 2 percent right trn).!5!.!:! > 4 '., ll,.2 JO 4 '.13 Ji,P e,,"'.2 "' Q. " ll. 8 c: g Q. ll c: 8 Fel Consmption ;"' '.f'.1,/ ;3.-"' />"--<!", 12 c: h._,,ff Q. ""'', " '<1'.115 c: " Delay.llO <i ::> LL Cycle length (Seconds),15

2 44 Transportation Research Record 819 Figre 4. Peak-hor traffic flows for Williamson Street in Madison, Wisconsin. _J -' "' z "' a: NNW.r"'= L 36 J+ -s: The differences in the findings from these stdies can probably be attribted to several factors. For example, both Baer (!) and Corage and Parapar (_) applied Claffey' s rate of idling fel consmption (!l to the delay estimates prodced by Webster's eqation and the TRANSYT model, respectively. However, these delay vales are estimates of overall delay, inclding that experienced dring deceleration and acceleration. Therefore, the reslting estimates of fel consmption attribtable to the vehicle idling component wold tend to exhibit an pward bias becase of the inclsion of a certain amont of non idling delay, especially when the proportion of stopped vehicles is high and average stopped delay is relatively small. Another significant consideration is that the nmber of stops at an intersection is a more important factor in fel consmption than is idling delay. For example, if one ses Claffey's composite fel-consmption coefficients of. 6 gal/vehicle-h of stopped delay and.1 gal/vehicle stop, a vehicle stop is eqivalent to 1 min of idling delay in energy se, even thogh a vehicle stop withot idling time cases less than 1 min of delay (for a 3-mile/h crising speed, one stop-and-go cycle withot idling delay cases abot a 15-s delay). n addition, the compter models that were sed to estimate the nmber of stops do not yield directly comparable reslts. The TRANSYT model sed in the Corage and Parapar stdy (2) can prodce an overestimation of fel consmption becase any finite delay is assmed to case a stop, even thogh in practice the vehicle involved may have ndergone only a small deceleration and acceleration. On the other hand, the NTSM simlation model acconts for the complete trajectory of each vehicle. However, becase midblock delay and fel consmption cannot be obtained separately from the NTSM otpt, it is also difficlt to isolate the intersection delay and fel consmption that are affected by trafficcontrol signals. When the midblock delay on the links is small enogh to be ignored, the NTSM otpts of delay and fel consmption can be considered to represent intersection traffic performance. Another possible case for the differing reslts of the previos stdies lies in the qee-discharge logic of the models that were sed. For example, the first vehicle in a qee accelerates directly p to crising speed whereas cars farther back spend considerable time traveling at speeds lower than the crising speed while moving p to the stop line. This type of movement generally consmes more fel than traveling at the crising speed. The microscopic qee-discharge behavior of the NTSM model atomatically incldes this effect, whereas the TRANSYT model ignores it. The effect of mltiple stops de to left-trning vehicles is also con- sidered in NTSM bt not in TRANSYT. Conseqently, in a strict sense, some of the otpt from these stdies cannot be directly compared. RSARCH OBJCTVS AND SCOP Given the above considerations, the primary objective of this research was to frther evalate the relations among delay, stops, fel consmption, and vehicle emissions for varios signal-control parameters at pretimed signalized intersections along an rban arterial nder existing roadway and trafficflow conditions (l). Althogh it wold be preferable to examine a variety of rban arterial scenarios, cost and time constraints limited the research to a single case-stdy rban arterial. The site selected for the stdy was a 5-ft section of Williamson Street in Madison, Wisconsin (see Figre 4). Williamson Street is an arterial that has signalized intersections spaced. 25 mile apart. Local street intersections occr between the signalized intersections, and traffic flow on the minor streets is relatively light compared with that on the arterial. Williamson Street is 5 ft in width and dring peak hors operates with two traffic lanes in the peak flow direction and one traffic lane pls a parking lane in the opposite direction. The experiments were designed to encompass a range of practical signal-timing plans developed by sing both maximal-band-width time-space diagram methods and version 6C of the TRANSYT compter optimization program <!!.> Parameters sch as directional priority, speed of progression, stop penalty, and split strategy were selectively varied. Both the TRANSYT and NTSM compter models were sed to simlate traffic performance nder each of the timing plans. The reslting data were then sbjected to statistical analysis by se of analysis of variance (ANOVA) techniqes. XPRMNTAL DSGN Research hypotheses were expressed in terms of the following seven qestions: 1. Do the manally designed signal-timing plans and TRANSYT-optimized timing plans yield significantly different levels of performance? 2. Do the TRANSYT and NTSM evalation models yield significantly different reslts? 3. What is the effect of cycle length on each MO? 4. Does the priority policy (peak-direction progression or balanced progression) make a significant difference? 5. What is the effect on MOs of speed of progression in developing the manal timing plans?

3 Transportation Research Record Figre 5. xperimental design for manally developed timing plans. Priority Policy and Speed of Progression (miles/h) Cycle Length Peak Direction Progression Be lanced Progression (s) Split Balanced V/C Balanced V /C Balanced V/C Balanced V/ C Balanced V/C Figre 6. xperimental design for TRANSYT-optimized timing plans. Cycle Length Peak Direction Progression (s) K 5 K = 6 K 9 Priority Policy and K-Factor Balanced Progression K = 5 K = 6 K = 9 6 BO What is the effect of the TRANSYT stop weighting factor on MOs? 7. Does the split strategy [balanced volme to capacity (V/C), or excess green to arterial with the minor street at level of service CJ make a significant difference? All of these qestions were to be answered for each of six MOs and two levels of aggregation (arterial and networkwide). The MOs were delay; stops; fel consmption: and hydrocarbon (HC), carbon monoxide (CO), and oxides of nitrogen (Nx) emissions. All signal-timing-control parameters were independent variables. Figre 5 shows the 6-cell experimental design matrix for th'e manally developed signal-timing plans. These timing plans were also sed as the initial timings for the TRANSYT model. The TRANSYT model then generated optimal timing plans that minimized the following performance index: P= an fnks (total delay)+ Kall f,'., ks (nmber of stops) (1) where P is the performance index and K is a stop weighting factor. Figre 6 illstrates the reslting 3-cell exper- imental design matrix for the TRANSYT timing plans. NTSM evalations were condcted by sing one replication per cell, each replication being a simlation of a 15-min time period. De to compter time limitations, the NTSM evalation of the manally developed timing plans was redesigned as a half-fractional factorial experiment. The previosly described hypotheses regarding the relations among the timing plans, the performance measres, and the evalation models were tested by sing ANOVA techniqes. Performance data were aggregated at two levels: arterial links only and the overall network. The presence of interactions between variables and then the main effects of all factors were investigated. A mltiple comparison of whether treatment (variable) means differed significantly was condcted after the ANOVA. These comparisons were made by testing the significance of particlar linear combinations of the variable means. The procedre sed was Dncan's mltiple range test with a.1 significance level. CALBRATON OF TRANSYT AND NTSM Prior to the se of the TRANSYT6C and NTSM compter models, a nmber of test rns were made for

4 46 Transportation Research Record 819 the existing evening peak-hor signal timing to calibrate those program-embedded parameters that showed significant differences from observed vales. This procedre also provided a means of validating the reslts of selected experiments throgh actal field measrements. The model calibrations were primarily concerned with the following parameters: start-p delay, lag, stop estimate, satration flow rate, free-flow speed, and amber phase response. The Wilcoxon matched-pairs signed-ranks test was sed, and no significant difference was fond between the TRANSYT. NTSM, and field-observed vales for the six selected traffic performance measres. Becase ot the differences in the way each model defines a link connecting two intersections, as well as the way in which performance statistics are accmlated, special procedres had to be followed to ensre consistency in the comparisons of TRANSYT and NTSM otpt. n TRANSYT, the delays actally incrred at the beginning of a receiving link and at the end of an approaching link are aggregated and assigned to the pstream link. This incldes all throgh and left- and right-trning movements. For this reason, the TRANSYT network does not se exit links. On the other hand, in the NTSM model, the link statistics are associated with the aggregate performance of all vehicles traveling from and to the respective stop lines that define the two ends of the link. since an internal link for both models encompasses some acceleration and deceleration delay, the accmlated statistics for the two models are not significantly different. For entry links, however, the difference between the two models is significant becase the TRANSYT entry link incldes both acceleration and deceleration delay at the stop line whereas the NTSM entry link excldes the acceleration delay at that stop line. As a reslt, the flow statistics for TRANSYT entry links are sally mch higher than those for NTSM. Therefore, for the NTSM and TRANSYT models to be consistent, the NTSM network was coded with exit links that wold accont for the acceleration delays incrred in departing the stop lines of exit nodes. A special adjstment to the TRANSYT otpt was also necessary in the calclation of the average speed on a link or for the overall network. For internal links, the average travel speed is obtained by dividing the distance traveled by the time spent on the link. Here, the time spent represents an actal travel time, inclding travel time for freef low speed, and niform and random delay. However, the program ignores the travel time on the entry link. Therefore, the time spent on the entry link is eqivalent to the niform pls random delays incrred in a qee. Conseqently, average travel speed cannot be calclated from the data for distance traveled and time spent on the entry link. For the same reason, the networkwide average travel speed cannot be obtained by dividing the total distance traveled by the total time spent for the network as a whole. Therefore, for the prpose of comparing TRANSYT and NTSM with respect to travel time and average speed, the TRANSYT otpt was adjsted by adding a reasonably estimated travel time to the niform and random delay on each entry link. The additional time spent for each entry link in the TRANSYT otpt was calclated by mltiplying the flow rate by its link length and then dividing by an average crising speed observed in the field. FNDNGS The experimental design involved for basic experiments: 1. TRANSYT evalation of manal timing plans, 2. TRANSYT evalation of TRANSYT-optimized timing plans, 3. NTSM evalation of manal timing plans, and 4. NTS M evalation of TRANSYT-optimized timing plans. Pairs of experiments were then copled together to become a modle for prposes of statistical and graphical comparison. The reslts of these comparisons are smmarized below. Traffic-Signal Parameters Verss MOs No interactions existed among the traffic-signal parameters. ach level of a traffic-signal parameter had a response crve that showed the same trend against each level of the other traffic-signal parameters. For example, total delay over each cycle length for K = 5 had the same trend as for K = 6 or 9, which showed that no interaction existed between K-factor and cycle length. The principal reslts derived from the individal experimental analyses for the case-stdy site are smmarized in Table 1 and discssed below: 1. Signal cycle length always had the most significant effect on each of the MOs. Within the range of 8- to 14-s cycle lengths, delay, fel consmption, and HC and CO emissions remained relatively constant. However, nmber of stops and NOx emissions decreased as cycle length increased. The greatest inefficiencies for all MOs occrred at the 6-s cycle length. This was de principally to the fact that, at cycle lengths less than or eqal to 8 s, the minor streets received more green than necessary becase of the minimm green interval constraint for accommodating pedestrians. 2. The stop weighting factor (K) sed in the TRANSYT optimization model was the second most significant variable in terms of delay, stops, and fel consmption. However, it had no significant effect on vehicle emissions. The nmber of stops and the amont of fel consmption decreased as the K-vale increased. However, delay in the overall network increased as the K-vale increased, whereas delay on arterial links decreased. This is becase of the trade-off between the delay to cross-street traffic and the delay to arterial traffic. The stdy reslts offered no evidence to spport the reported hypothesis (2) that a K-vale of 6 wold provide a minimm fei=-consmption timing plan. ven thogh this stdy showed that a K-vale of 9 yields the minimm fel-consmption timing plan, this reslt is not sfficient to jstify the conclsion that this wold occr in every sitation. 3. Speed of progression, as sed in the manal signal-timing method, was the third most inflential variable. t had a significant effect on total delay and nmber of stops. However, it also had some effect on fel and vehicle emissions. The higher speeds of progression prodced the lower MO vales. 4. Priority policy, as sed in the manal signal-timing method, had a greater effect than the split method, bt the significance of both variables was considered to be negligible. Usally the "peakdirection priority" and "excess green time to arterial" options wold slightly redce the delay to arterial traffic. The other MOs were also redced by sing the peak-direction-priority option. Manal Verss Optimized Timing Plans n general, TRANSYT-optimized timing plans were fond to improve all MOs both on the arterial links

5 Transportation Research Record Bl9 47 Table 1. Smmary of interactions between signaltiming parameters and MOs. Timing Method Parameter Total Delay Stops missions Fel Consmption HC co NOx Manal Cycle length (j) (j) (j) (j) (j) (j) Speed of progression + (j) Priority policy Split method + TRANS YT Cycle length (j) (j) (j) (j) ll ll K-factor (j) (j) ll Priority policy + Note: +=main effect detected from TRANSYT otpt, and ti= main effect detected from NTSM otpt. Table 2. Relative benefits of optimized timing plans: ave rage percentage i m- provement at 12-s cycle length. Total No. of Fel missions (%) Delay Stops Consmption Area (%) (%) (%) HC co NOx Arterial Network Table 3. Average percentage difference in MOs from TRANSYT and NTSM models at 12-s cycle length. Total No. of Fel missions(%) Delay Stops Consmption Area (%) (%) (%) HC co NOx Arterial Network and the overall network, especially in the range of BO- to 14-s cycle lengths. The improvement of the optimized timings verss the manal timing plans increased as signal cycle length increased. The nmber of stops and total delay showed the most change: fel consmption and vehicle emissions were less sensitive to the timing methods. For example, Table 2 gives the improvements fond for a 12-s cycle length. The percentage differences given in the table were compted from NTSM simlation data and represent the relative improvement of the optimized timing plans with respect to the manally developed timing plans. The ability of the TRANSYT model to generate improved signal-timing plans compared with traditional methods has, of corse, been noted and reported before. TRANSYT Ve css NTSM xcept for the 6-s cycle lengths, the MOs estimated by the two models were fond to vary in a similar manner as cycle length ranged from BO to 14 s and the NTSM model was fond to prodce the larger MO vales. At the 6-s cycle length, almost all arterial links were oversatrated. Under these conditions, TRANSYT was fond to generate very large delay estimates compared with NTSM. The average difference in the MOs estimated by the two models increased as signal cycle length increased within the range of 8-14 s. Average differences between the MOs evalated by the two models are given in Table 3 for a 12-s cycle length. xcept for HC emissions, all MOs estimated by NTSM have larger vales than those prodced by TRANSYT. The particlarly large difference in delay for the arterial links may be cased in part by the difference in definition of the TRANSYT link and the NTSM link. Becase the MOs estimated by the NTSM model accont for not only signal-related effects bt also midblock interference, the NTSM model shold prodce higher vales for the MOs. The models also differ in their estimates of fel consmption and vehicle emissions. These differences are probably de in large part to the natre of the fel-consmption and emissions sbrotines within each model. The difference between the TRANSYT and NTSM models for the varios MOs might also be attribted to some extent to the error cased by having only one NTSM rn for each cell of the experimental design matrices. The NTS M ser's manal sggests at least two replications for each cell. A limited nmber of test rns condcted by sing different random seeds for one of the cells showed a range in total network delay of approximately 7 percent and a slightly higher percentage for arterial delay. The range of total delay is almost the same size as the previosly determined percentage change in the MO de to the se of different K-factors bt far less than that de to the model sed. Relations among MOs Possible relations among the varios MOs were of interest becase this information wold indicate whether one MO cold be sed as a srrogate indicator for the others. Becase cycle length was shown as the dominant signal-timing parameter, a comparison of each MO over cycle length was made to reveal any correlation or similar response pattern among MOs. Figre 7 shows the relation over cycle length of varios network MOs as evalated by the TRANSYT model. As shown in the figre, total delay, fel consmption, and HC, co, and total polltants (combination of HC, CO, and NOxl fall into a category that shows the same response pattern over cycle length. However, nmber of stops and NDx emissions fall into another category that shows a steady decrease in the MO as the cycle length increases. Figre 8 shows similar relations between cycle length and network MOs evalated by the NTSM model. Table 4 gives regression eqations developed from the data in Figres 7 and B. From the car relation coefficients for the NTSM data, it is apparent that total delay is strongly associated with fel consmption and HC, co, and total emissions. The high correlation between total delay and fel consmption implicitly spports the finding reported by Cohen and ler () in their stdy of an isolated intersection: that delay and fel consmption are minimized at approximately the same cycle length. Of the individal polltants, only NDx emissions were fond to be well associated with nmber of stops. Fel consmption and total delay were both reasonably well correlated with nmber of stops. CONCLUSONS At the otset of the research, there were several fndamental qestions to be resolved. The concl-

6 48 Transportation Research Record 819 Figre 7. Relation among network MOs generated by TRANSYT. figre 8. Relation among network MOs generated by NTSM , 2 > 'ij!ooo. "'. "' g 1il j 35. "' ] 1 j '?.; 6oo > r "' ' g _!!,O.! "' :.--- al Q._ il " "' 1 lil., 2 "'.. "' '2 :i Boo 1 '? ;a!: 6oo >.. al "'4 P.,ol i : Con mptlon bo Oi' - _:top 8 i g i., RC fl " ' "'-,, c;q ' C;ycle Length (sec) C;ycle Length (sec) Table 4. Regression eqations for relations among MOs in network. TRANSYT NT SM Correlation Correlation MO s qation Coefficient qation Coefficient FC verss TD FC = O.Ol 2TD.973 FC = TD.958 HCverss TD HC = l.6td.998 HC = TD.96 CO verss TD CO= TD.997 CO= TD.976 TPverss TD TP = l 9.8TD.996 TP = TD.97 HCverss NS HC= 119+ l.21ns.524 HC= NS.766 NOx verss NS NOx = NS.939 NOx = NS.743 TP verss NS TP = NS.559 TP = NS.739 TD verss NS TD= NS.488 TD= NS.584 FC verss NS FC = !NS.615 FC = NS.755 Note: FC =fel consmption (gal), TD = total delay (vehicle-min), HC =hydrocarbon emissions (g), CO= carbon monoxide emissions (g), TP =total polltant emissions (g), NS = nmber of stops (vehicles), and NOx =nitrogen oxide emissions (g). sions that can be drawn with respect to these qestions are smmarized below: 1. Among the varios signal-timing parameters, cycle length and K-factor in the TRANSYT performance index are the most significant variables that affect the MOs. However, the stdy failed to identify an optimal vale of K that wold prodce a minimm level for each MO. Speed of progression is highly correlated with nmber of stops: A higher vale yields a lower nmber of stops. Priority policy and split method did not show a significant impact on any of the MOs. 2. All MOs can be improved, especially stops anp delay on the arterial links, when optimized timing plans are sed instead of those developed by se of traditional time-space diagram methods. 3. When the TRANSYT6C and NTSM simlation models are compared, the NTSM model prodces similar, bt larger, vales for the MOs nder a given signal-timing plan. This is probably de simply to the differing simlation logic within the two models. 4. There appeared to be many correlations or similar response patterns among the varios MOs. Nmber of stops and NOx emissions showed a close correlation, whereas delay appeared to be a strong srrogate for the other principal MOs. Becase the stdy was performed for a single case-stdy site that had a niqe set of traffic flows, it is nknown whether different traffic and roadway conditions wold lead to significantly different reslts. n addition, the reslts of this stdy were limited in that it was only possible to condct one replication for the cells of the experimental design matrices. This cold create a large sorce of error or a loss in the power of the tests. Therefore, frther research cold be focsed on mltiple replications of other scenarios with different roadway and traffic conditions. RFRNCS C.S. Baer. Some nergy Considerations in fie Signal Timing. Traffic ngineering, 1975, pp Traf Feb.

7 Transportation Research Record K.G. Corage and S.M. Parapar. Delay and Fel Consmption at Traffic Signals. Traffic ngineering, Nov. 1975, pp D.. Robertson and P. Gower. TRANSYT: A Traffic Network Stdy Tool. Road Research Laboratory, Crowthorne, Berkshire, ngland, Rept. 253, P.J. Claffey. Rnning Costs of Motor Vehicles as Affected by Road Design and Traffic. NCHRP, s. Rept. 111, S.L. Cohen and G. ler. Signal Cycle Length and Fel Consmption and missions. TRB, Transportation Research Record 677, 1979, pp R.D. Worrall and. Lieberman. Network Flow Simlation for Urban Traffic Control System, Phase : Volme 4--User 's Manal for UTCS-1 Network Simlation Model. Office of Research and Development, FHWA, Final Rept. FHWA-RD-73-86, May c.-. Do. An valation of Traffic Flow Performance Measres in a Linear Arterial Network. Univ. of Wisconsin, Madison, Ph.D. thesis, A.D. May. TRANSYT6C Model Workshop. nstitte of Transportation Stdies, Univ. of California, Berkeley, 1977.