2010年--停車庫(kù)外文翻譯--微觀仿真在城市停車設(shè)施規(guī)劃中的應(yīng)用

1、<p>　　中文6400字,4000單詞，2.1萬(wàn)英文字符</p><p>　　出處：Sykes P, Falco J D, Bradley R, et al. Planning urban car park provision using microsimulation[J]. Traffic Engineering & Control, 2010, （51）.103-107</p>

2、;<p>　　Planning urban car park provision using Microsimulation</p><p>　　Pete Sykes, Falco De Jong Richard Bradley, Gerard Jennings, Greig McDonnell</p><p><b>　　Abstract</b><

3、;/p><p>　　In three different locations around the world, city planners have sought to investigate the effects on the road network of car park planning policy and accessibility. All have looked for methods to mi

4、nimise urban congestion caused by drivers searching for a car park space. All have used an SParamics microsimulation model to test the design options. This paper describes how they went about it and what they achieved.&l

5、t;/p><p>　　INTRODUCTION</p><p>　　The provision of available car parking is one of the most contentious issues for city drivers. Car park spaces can be hard to find and expensive to use. There may b

6、e queues to get into the most convenient car parks which require drivers to move on to alternative car parks. Some city centre traders regard the lack of suitable car parking as a significant reason for shoppers to prefe

7、r out of town shopping centres. In New Haven Connecticut, Gov. Rowland at a ceremony celebrating the renovation of t</p><p>　　Car park hunting, the circulation of drivers looking for a parking space, can be

8、a major contribution to city centre congestion. The proportion of cars searching for a space was found to be 26% when surveyed in Manhattan in 2006, while in Brooklyn it was 45%. The situation is not new. In 1927, a simi

9、lar survey in Detroit found the figures to be 19% and 34% in separate locations [2]. This long standing problem may at last be assisted by technology. While iPhone users can now notify each other as </p><p>

10、　　Car park location in urban planning policy is largely concerned with optimising the relationship between car parks, drivers and their destinations. Charging regimes may be used to reduce localised inconvenience caused

11、by parked cars and to favour one class of driver over another in allocating spaces. The perceived benefits include improvements to a city’s commercial centre through better accessibility for the target consumer. Policies

12、 may be supply- led by actively managing spaces or demand-led </p><p>　　Urban planning policy considers the charging regimes for car parks. Transport planning policy complements this and considers access to

13、the car parks. It is concerned with the relationship between car parks, the road network and congestion.</p><p>　　Accessibility of car parks is addressed in road design guidelines. UK Department for Transpor

14、t advice on parking guidance and information systems includes reports of case studies that show that there are quantifiable benefits to be derived from installing variable message signs indicating car parking space avail

15、abilty [6]. Benefits are described as quantitative, in terms of time saved, and qualitative in terms of public image and driver safety. WebTAG [7] guidance touches on the subject briefly </p><p>　　A study in

16、 Nieuwegein (The Netherlands) modelled a large expansion in travel demand and the provision of car park spaces for a major town centre redevelopment, where Saturday afternoon shopping was the critical period. It incorpor

17、ated ITS within the microsimulation model to deliver information to drivers on availability of spaces and routes to car parks. Another study, in Rochdale (England), models the distribution of spaces in conjunction with m

18、ajor town centre development plans. The goal is to</p><p>　　CAR PARK MODELLING IN MICROSIMULATION</p><p>　　Typical design option tests for a microsimulation model include changes to road layout,

19、 public transport priority schemes, optimisation of signals, or changes in demand. Each individual vehicle in the simulation will react to these changes, and the congestion they cause, as it moves to its destination. <

20、;/p><p>　　When testing the effect of car park policy decisions, the emphasis moves from examination of the effect of changes to the road network to examination of the effect of changes in the destination for th

21、at part of the trip undertaken in a car. The simulation model must now include the capability to distinguish between the driver’s destination and the vehicle’s parking location and make dynamic choices between these loca

22、tions.</p><p>　　Figutr 1：Rochdale city centre carparks</p><p><b>　　Arrivals</b></p><p>　　Car parks are an entity within the microsimulation model, and are linked to zone

23、 destinations and car parks may serve more than one zone. Allocation of vehicles to car parks is undertaken by limiting car park access to specific trip purposes. </p><p>　　The model includes car parking cha

24、rges and the distances between car parks and associated zones as components of the generalised trip cost. As each vehicle type may have different cost coefficients, the modeler may differentiate between drivers who will

25、accept a longer walk and those who will accept a higher charge.</p><p>　　If a car park is full then vehicle drivers within the simulation wait at the entrance for a predetermined time, after which they re-as

26、sess their choice of car park and possibly proceed to another. Using an external software controller it is possible to monitor car park occupancy within the simulation and change a vehicle’s destination before it reaches

27、 the queue.</p><p>　　As an example of how this methodology can be used to implement a car park policy model, consider a city centre zone with a mix of retail and commercial use with several car parks availab

28、le within reasonable walking distance. Drivers will have a preferred location based on their proposed length of stay and the car park charging structure.Some drivers may have a contract for permit parking. A car park may

29、 have multiple adjacent entrances, each coded with a restriction to force vehicles to accept t</p><p>　　may be modelled by adjusting the proportion of driver and vehicle types using a particular zone and rel

30、ated car parks.</p><p>　　Departures</p><p>　　The assignment of all vehicles to an S-Paramics road network is controlled by a detailed (5 minute) time release profile. In its simplest form of use

31、, the journey origin car park is determined by finding the minimum journey cost,which includes the walk time, or vehicles may simply be released in proportion to the size of the car park.</p><p>　　If more co

32、ntrol is required, such as the ability to match departures to arrivals at the same car park, the release may be triggered by an external software controller linked to the simulation model which uses an algorithm to deter

33、mine when to release vehicles and where they originate on the network. This may be associated with a car park occupancy monitoring system and be used to match vehicle arrivals with a subsequent departure.</p><

34、p>　　NIEUWEGEIN PLANNED DEVELOPMENTS</p><p>　　Nieuwegein is a town just to the south of the city of Utrecht in the centre of the Netherlands with good economic prospects. To make the most of this, the muni

35、cipality wants to restructure their city centre to include new developments. New multi-story car parks are planned to cope with the increased demand for parking spaces and a system for dynamic parking advice will attempt

36、 to minimise queueing at the car park entrances.</p><p>　　Grontmij was asked to build an S-Paramics model of the city centre to review the effects of the new developments on the city’s road network including

37、 the parking advisory system. The results of the simulation showed congestion at the three car parks closest to the city centre. This was in accordance with the city manager’s expectations. To bring the remaining capacit

38、y of the two other car parks into use, a parking advisory system was implemented in the simulation to redirect vehicles to the avai</p><p>　　MODELLING SOLUTIONS</p><p>　　Data collection</p>

39、;<p>　　Travel demand matrices for the Nieuwegein model were derived from a pre-existing macroscopic model and refined with survey data. Further surveys were undertaken to determine the usage of the main car parks,

40、 the average length of stay and the residual numbers after the shops were closed.</p><p>　　Because of the complex requirements of the Rochdale and Takapuna studies, more extensive data collection was necessa

41、ry. Demand matrices for Rochdale were developed primarily from roadside interview data which identified the true destination of the journey. The interview data was used to determine the parking type (eg long/short stay,

42、on/off street, contract), and the likely parking duration (based on journey purpose).</p><p>　　A full parking inventory of the town centre and occupancy data provided input to a car park location model, used

43、 to link car parks to destination zones. The time to walk to each destination from each car park was initially estimated from simple geometry and later used as an important calibration parameter.</p><p>　　Pa

44、rking inventory data was essential to provide accurate capacity estimates categorised by: short or long stay, on or off-street; public or private, and charged or free. This included all car parks within, or adjacent to,

45、the town centre. Residential parking areas adjacent to the town centre, were also included as these provided free parking, with longer walk distances, and were often used by commuters. Areas with high drop-off trips were

46、 modelled as a private parking type at their destination b</p><p>　　The car park data also provided charging information for each car park. When combined with the car park interview data it was found to be p

47、ossible to group charges into a single short stay and long stay charge. This simplification was appropriate in Rochdale, but it would have been possible to use a car park specific charge if the variation was more signifi

48、cant. </p><p>　　Parking data in Rochdale was limited to peak car park occupancy and the model would have benefited from a comprehensive survey of vehicles entering and leaving throughout the day. In Takapuna

49、, and Nieuwegein, arrival time, dwell time, and occupancy data was available and this was used, with the charging information, to model driver’s choice of car park.</p><p>　　Demand matrix segmentation</p&

50、gt;<p>　　Matrix segmentation enables the modeller to control departure time demands for different classes of vehicles. The degree of segmentation must be supported by the data. Similarly car parks should be groupe

51、d to a level commensurate with the detail of model input data.</p><p>　　In Rochdale, matrices were derived for cars categorized by commuter, non-commuter and work. The interview data enabled the commuter and

52、 work matrices to be subdivided into private non residential (PNR) parking and contract parking. PNR parking supply, which is notoriously difficult to estimate, was assumed to be unlimited in the model as the matrixes we

53、re explicity defined. For areas outside the town centre all drivers were assumed to park at their destinations.</p><p>　　Takapuna adopted a similar approach, with demands segmented into ‘long stay’ and ‘shor

54、t stay’ parking types. Long stay was further split into ‘on site’ or ‘general’ depending on access to workplace car parking. Long and short stay demands were estimated from the purpose matrices of a strategic transport m

55、odel, with adjustments based on car park number plate and turn count data.</p><p>　　Trip linking</p><p>　　In order to model vehicles arriving and leaving from the same car park, trips in and out

56、 of the city centre area must be linked, with origin car parks and departure times selected based on prior arrival car parks and times. This requires more sophisticated control over the departure time and the departure l

57、ocation than is available from conventional OD matrix methodologies.</p><p>　　In Nieuwegein, the study period covered the main shopping peak period on Saturday afternoon and included a warm up period to popu

58、late the car parks and initialise the ITS controller within the microsimulation model. To model the linked trips, all traffic departing from the city centre was deleted from the OD matrices. An external software controll

59、er monitored the car park occupancy in the model to determine the arrival profile and, after a suitable dwell time typically one hour, released matchin</p><p>　　For Takapuna, before the PM simulation was run

60、, a separate demand model was used to generate a profile of releases in the PM peak based on car park occupancy derived from the AM peak. This was to be subsequently used in the PM model run. </p><p>　　Matri

61、ces were generated based on the profiled demands derived for each zone within Takapuna. When selecting from which particular car park the vehicle should depart, the demand model matched its outbound zone with that of a p

62、arked vehicle. The match was made based on the parking duration (long/short), and the expected departure time estimated from the arrival time distribution. If this process found a vehicle within 20% of its expected depar

63、ture time, then one was added to the profile to be rele</p><p>　　The Rochdale model was also divided into AM and PM peak scenarios. The choice of car park for departure in the PM peak was modelled using the

64、generalised cost of travel combined with an exit cost to help bias car park selection. Comparison with observed data showed the use of exit costs could successfully calibrate the model. </p><p>　　ROCHDALE KE

65、Y DEVELOPMENT AREAS</p><p>　　Rochdale is a historic market town in East Lancashire, now a metropolitan borough of Greater Manchester. The town centre contains a central retail and business area that not only

66、 competes with other local centres but also with Manchester City Centre and the Trafford Centre. To help improve Rochdale’s competitiveness as a centre for retail and commerce, Rochdale Development Agency plan a major re

67、development of the south east area of the town centre. Comparative assessment of the town centre maste</p><p>　　Car park searching</p><p>　　As the primary goal of all three models was to study t

68、heimpact of car parking on urban traffic congestion, the strategies for allocating vehicles to car parks within the model and subsequent car park hunting were key to the success of the studies.</p><p>　　The

69、Nieuwegein study was specifically designed to test the effect of a proposed car park advisory system. VMS signs were positioned on all approaches to the town centre (Fig 2) and provided information on which car park to s

70、elect (Fig 3). Based on the experience of the Town Parking Manager, the ITS system was configured to make only 20% of drivers follow the advice of the parking advisory system. This left the remaining 80% to proceed to th

71、eir first choice car park, and re-route from there if it</p><p><b>　　was full.</b></p><p>　　Figure 2：City centre model of Nieuwegein</p><p>　　The Rochdale and Takapuna m

72、odels focussed on accessibility more than guidance. In Rochdale, policy decisions were used to determine car park choice, which restricted the allocation of car parks to zones, eg contract parking areas were linked to wo

73、rk-related, rather than shopping-related zones. In Takapuna, parking was less constrained by policy and all car parks were linked to all zones with less predetermined allocation of spaces.</p><p>　　Vehicles

74、arriving at a car park would queue for a fixed time then move on to the next best car park based on the vehicle trip cost, walk cost, and parking cost. The Takapuna model augmented this selection process with a car park

75、availability control and a search limit implemented through an external software controller. The search limit reflects drivers’ willingness to circulate through numerous car parks in order to find a parking space, by set

76、ting a limit on the number to be tried before givin</p><p><b>　　ISSUES</b></p><p>　　First choice car park</p><p>　　The first choice car park issue arose in both Rochdale

77、 and Takapuna. This occurred when the first choice car park for a vehicle was small and quickly filled. Most vehicles that selected this car park to minimise journey costs would have to re-route to their second choice. I

78、n reality many drivers make the same trip regularly and learn that this car park is usually full. They discount it as a first choice and select a larger car park with a better chance of finding a space.</p><p&

79、gt;　　Two solutions were developed to address this problem.Takapuna used their car park availability controller to overwrite the driver’s choice, to provide a proxy for the driver learning process. The second solution was

80、 to group smaller car parks, typically those with less than 50 spaces, to avoid excessively large demand being allocated to a specific car park as first choice. Car park destination catchments and walking times were adju

81、sted in parallel to prevent too many vehicles having the same fir</p><p>　　A high level of matrix segmentation is in use for Rochdale to allocate car parks. This methodology offers a solution through further

82、 segmentation by driver ‘familiarity’. Drivers with knowledge that the smaller car parks are full tend to avoid them and make their first choice the larger car parks.</p><p>　　THE MODELS IN OPERATION</p&g

83、t;<p>　　Nieuwegein</p><p>　　The base model was calibrated for a typical Saturday afternoon.The test scenarios included planned new developmentsin the city centre.</p><p>　　Two future year

84、 simulations were compared. In thefirst, without the advisory system, large queues formed at the most attractive car parks. With the parking advisory system, the vehicles were more equally distributed over the available

85、parking capacity with spaces available in most car parks. This resulted in fewer vehicles searching for a parking spot and fewer queues in the city centre. This was achieved with just 20% of drivers taking heed of the pa

86、rking advisory system. Future tests will exte</p><p>　　As the model simulated a future year scenario of 2015 and the detailed design of the town centre will probably differ from that which has been simulated

87、, the predicted benefits were conservatively interpreted in evaluating the scheme. Despite that, the conclusion was that an investment in a parking advisory system to make the best use of the parking capacity in Nieuwege

88、in is justifiable.</p><p><b>　　Rochdale</b></p><p>　　The Rochdale model has been used to test part of a major town centre redevelopment planned for 2012. Key features include relocat

89、ing the town centre bus station and council offices, removing the main town centre multi-storey car park, and redesigning key junctions on the A58 through the town centre. </p><p>　　Further developments in t

90、he modelling process may include a similar external software controller as implemented in Takapuna and Nieuwegein. This could include explicit car park guidance at route decision points as well as a car park space ‘oppor

91、tunity cost’ which would be added to the generalised cost associated with each car park before the first choice is allocated. This cost would bring the likelihood of space availability at each car park in to the destinat

92、ion choice although more survey dat</p><p><b>　　Takapuna</b></p><p>　　The S-Paramics model for Takapuna is to be the operational transport assessment tool for all significant plannin

93、g applications and district plan change proposals within central Takapuna. In this way, all assessments will be made under a common and consistent modeling framework as has previously been successfully achieved in two ot

94、her developing areas of North Shore City.</p><p>　　With the recent economic downturn, some landowners in the area have ceased trading or sold their holdings, while others have delayed their planning and asse

95、ssment work. Takapuna has a calibrated car park model incorporated into the traffic simulation model,both of which are part of an assessment framework that is ready to be used as soon as economic growth returns.</p>

96、;<p>　　6 SUMMARY AND CONCLUSIONS</p><p>　　Car parking strategy is crucial to reducing the problem of traffic congestion in urban centres. The Nieuwegein study has demonstrated how effective even a par

97、tial take up of a particular solution can be Tests undertaken with the Rochdale and Takapuna models show that the ability to include car parking strategies within the analytical framework can significantly influence the

98、 outcome of design solutions.</p><p>　　Each study has identified the necessity of linking arrival and departure trips from the same car park, but developed three distinct solutions for this. The way in which

99、 simulated drivers select a car park has also varied, but has been consistently based on matrix segmentation, enabling charges and restrictions to be applied to reflect charging policies. The issue of identifying a sensi