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中国科技论文在线 Road Structures, Traffic Demand and Flow

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中国科技论文在线 Road Structures, Traffic Demand and Flow 中国科技论文在线 http://www.paper.edu.cn Road structures, traffic demand and flow characteristics at inhomogeneous urban freeway sections# HE Shuyan, GUAN Wei** 5 (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044) Abstract: IRoad inhomogeneities such as on- and off-ramps, lane increasing and decreasing are potential bottlenecks. Capacity of urban freeway during peak period is affected by these complex road structure and high traffic demand. Since the dense inhomogeneities are close to each other, investigation aiming at describing integrated effects of road inhomogeneities is needed. In this paper, 10 based on real traffic data of Beijing urban freeway, relation between traffic demand, road structure and lane-specified traffic characteristics are investigated. It is turned out that the main reason for capacity decrease at peak period is fluctuations induced by competition between main road and ramp. However, on-ramp and off-ramp are different in the way for triggering a capacity drop. Based on empirical investigation, mathematical descriptions for congestion evolution process are developed for further 15 modelling. Key words: traffic flow; transportation system, empirical investigation; urban freeway; 0 Introduction Traffic congestion and its related capacity drop have been researched for several decades 20 since traffic demand increased in metropolitan. Empirical research began from monotonically decreased density-velocity relations on highway traffic[1-3] to complex traffic state identification[4-7] or analysis based on various traffic characteristics [8-11]. More realized case such as multi-lane and inhomogeneities are also considered[12-17]. However, lane deviation in main road is less considered compare to other features because in 25 congested traffic, flow is almost synchronized among lanes. Actually, an off-ramp bottleneck is usually formed by queued vehicles at upstream shoulder lane which is occupied by overstuffed vehicles [16,18]. In this situation, lane deviation is significant and dominated by traffic demand. Hence the relation between lane flows and traffic demand at immediate upstream of off-ramp is important for understanding off-ramp bottlenecks. 30 Moreover, a large number of works were concentrated on highway traffic with scattered inhomogeneities which can be separated into several isolated bottlenecks. Urban freeway is characterized by complex road structures which serves as dense inhomogeneities: on-ramp, off-ramp, lane decreasing and increasing, upgrading and degrading, or bent road with lower speed limitation. The dense inhomogeneities affects each other, such that investigation should 35 be taken on an integrated view by considering a series of related inhomogeneities. In this paper, relation between traffic demand, road structure and lane-specified traffic characteristics are investigated based on real traffic data collected from a 4.5km long urban freeway with 12 on- or off-ramps. It is found that competition between ramps and main road is main reason for capacity drop at urban freeway. Based on this fact, detailed analysis on the 40 emergence of on-ramp and off-ramp fluctuations is implemented. The analysis take traffic demand and its related lane-changing rate as important parameters. Mathematical descriptions are also provided for further modelling. However, here we mainly focus on relations between Foundations: Ph.D. Program Foundation of China Ministry of Education (No. 20070004020) Brief author introduction:HE Shuyan (1980-), Female, Ph.D. candidate, intellegent transportation system and traffic flow theory Correspondance author: GUAN Wei (1968-), Male, Professor, intellegent transportation system and artificial intelligence. E-mail: [email protected] - 1 - 中国科技论文在线 http://www.paper.edu.cn traffic characteristics and traffic demand without detailed discussion on traffic dynamics. Although such a description is not complete, it can be integrated to some known traffic 45 dynamics models for multi-lane extension. 1 Data collection and road structures Single vehicle data are collected in June 2007 at a segment from Xizhimen to Deshengmen of Beijing 2nd Ring Road, including 11 main lane sections and 12 ramp sections (including 6 on-ramps and 6 off-ramps). Detectors are settled at on- and off-ramps and their main road 50 vicinities. The schematic road structure of these sections is shown as Fig.1. Two-minutes data are provided for analysis. x(m) M11(3968) M10(3768) M9(3424) M8(2989) M7(2290) M6(1979) M5(1299) M4(1124) M3(606) M2(157) M1(0) R12(4455) R11(3815) R10(3505) R9(2949)R8 (2714) R7(2549) R6(2149) R5(1939) R4(1253) R3(794) R2(590) R1(110) R8-2 R8-1 Fig. 1 Schematic road structure for a segment of Beijing 2nd ring road. 2 Freeway capacity with inhomogeneities 55 2.1 Road capacity Time-space diagram of average road speed is shown in Fig.2. During morning peak there are one complete congestion area M11~M4 where vehicles were moving slowly and its upstream and downstream were both free flows. The rest two sections, M2 and M1 were downstream of another congestion area which are not completely displayed. 60 Fig. 2 Time-space diagram of average velocity. The morning peak can be divided in to several period. From 6:31 to 7:07, congestion emerged at M11, then propagated to M4 with an average speed of 4.7km/h. Congestion of the downstream front M11 was homogeneous till 7:25 when a moving jam propagated from 65 downstream which can not be traced from our data collection. From 7:25 to 8:23, sections M11~M4 were all in congestion but with a lower downstream capacity. At about 8:24, section M4 and M8 gradually recovered to free flow because their upstream M2 and M7 were blocked by the queued vehicles at the shoulder lanes. Such that vehicles traveled at other lanes which wanted to exit at the next off-ramp had to run slowly waiting chance to insert shoulder lane. For conveniens, 70 In this paper, we number the lanes from medium lane to shoulder lane as increased number from 1 - 2 - 中国科技论文在线 http://www.paper.edu.cn to the lane number. Hence at M2 and M7, the shoulder lanes had the slowest speed while lane1 was in the highest speed, meaning that fluctuations induced by shoulder lane queue extended to other lanes for the large number of vehicles want to exit main road at next off-ramps R2 and R7, separately. Finally at 9:06, after M8 recovered to free flow, M9 also recovered. 75 As a summation, there are three major periods during the congestion evolution process at morning peak 7:00~9:00. Period 1 is from 7:07 to 7:25 when sections M11~M4 were all in congestion with a temporal stable downstream front; Period 2 is from 7:25 to 8:23 when capacity of downstream front was decreased due to fluctuations propagated from downstream; Period 3 is from 8:24 to 9:06 when the whole congestion area was partly dissolved and divided into two parts 80 M11~M8 and M7~M6 for severe congestions happened upstream. The three periods can be understood as a result of growing fluctuations in dense flow. First, at section M11, compare the foreign jam (Jam B in Fig.2) propagated from some unknown downstream at 7:25 to the moving jam B, which was generated at M4 and reached M2 at 7:20 (Jam A in Fig.2), the similarity means that jam B was probably generated from another fully 85 developed congestion area downstream. Hence if take period 1 as a self-maintained congestion period, period 2 can be seen as a mutual-affected period for the “imported” fluctuations from other congestion area. Period 3 is formed by local capacity decrease due to shoulder lane queue. As shown in Fig.3, shoulder lane flow at M2 and M7 kept a high flow state with high density, while after those before 8:04 and 8:24, separately, then flow rate dropped without obvious reason. From 90 the flow-time curve, fluctuations are expanded from shoulder lane to inner lane. Hence the fluctuations were possibly com from spill-back queues from congested downstream off-ramps. However, the dense flow itself has less stability which is easy transited from homogeneous flow to stop-and-go flow. 95 Fig. 3 Time-dependent flow rate and velocity of M2 and M7. (lane1: ---, lane2:- -, lane3:-.-). Both downstream propagated fluctuation and local capacity restriction may cause road capacity decrease. Because there was no obvious temporal changes of traffic state in each period, the mean values during every whole period can reflect traffic situation properly. From period 1 to period 3, the mean flow rate of the whole congestion area decreased from 1438 vpl/h to 1184 vpl/h, 100 meaning a decreased capacity with congestion evolution. The above analysis distinguish three congestion periods with time evolution during morning peak, showing a gradually decreased road capacity although some sections recovered to free flow at last. The capacity decrease was mainly caused by two reasons, one is downstream fluctuations generated from another congestion area, the other is local capacity decrease for the queued ehicles - 3 - 中国科技论文在线 http://www.paper.edu.cn 105 at off-ramp upstream. 2.2 Congestion emergence from maximum capacity state From empirical observation in Fig.2, it is found that spontaneous congestion is likely to begin at downstream of merges (on-ramp or lane decreasing area) such as M2 and M12 where traffic flow is increased by merging vehicles. A possible explanation is as follows: When vehicles insert 110 into a platoon, drivers can temporally accept short headway than usual to maintain a smooth moving. Then the short headway will relax to a comfort one after a while and cause a slightly capacity reduction at somewhere downstream. The fluctuation back-propagated are amplified, leading to traffic breakdown at last.
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