The Use of Modern Data Collection Technologies to Inform the Development of a Traffic Model and Asset Management Plan for Palmerston North

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1 The Use of Modern Data Collection Technologies to Inform the Development of a Traffic Model and Asset Management Plan for Palmerston North Simon Cager Infrastructure Investigations Engineer Palmerston North, NZ Alan Kerr Technical Director, Transportation Wellington, NZ Abstract Palmerston North City Council has recently commissioned a new traffic model to cover the City and surrounding area. The model is being used to assess the performance of the road network now and in the future. It is being used to assess infrastructure as well as land use and policy changes. It is also being used to assess the City s Asset Management Plan. As part of the model development, Council (supported by Beca) used a range of modern data collection techniques to understand current traffic patterns around the city. This included the use of Fleet GPS and Bluetooth data. Both techniques have proven to be a very cost effective means to collect large volumes of data, and the Palmerston North model was the first in New Zealand to use these techniques extensively. Council is also using some of this data to inform signal operations around the city. This paper provides a background to the study and the technology adopted before demonstrating the value that can be gained from applying these techniques across a variety of contexts. Key Words Palmerston North, Traffic Model, Bluetooth, Fleet GPS, Asset Management Plan Introduction Traffic engineering has traditionally been, and continues to be, a data hungry discipline. Traffic engineers use information such as traffic flow data, journey time data and crash statistics to inform designs and influence investment decisions. These data sources form an important component of the development of traffic models and asset management plans, which are the subjects of this paper. Traditional means to collect traffic data include road sensors (such as loops or tubes), household travel surveys, manual counts, floating vehicles and traffic cameras. This information can be expensive and time consuming to collect. New technology such as infrared sensors, video analytics, WiFi, Bluetooth and in car GPS can collect larger volumes of data more cost effectively. However, with this come challenges larger volumes of data take longer to clean and process, and require more computational power to undertake meaningful analysis. New sources of data, coupled with existing ones, present a real opportunity to traffic engineers involved with asset management and traffic modelling. Palmerston North City Council has recently developed a brand new traffic model of the city and surrounding area (the Palmerston North Area Traffic Model, or PNATM), alongside a new Asset Management Plan (AMP). Both exercises have proven to be an opportunity to utilise new forms of data. Context As stated above, new traffic data collection approaches represent an opportunity to collect more data more quickly and for less money. Traffic models such as the Tauranga Traffic Model and the Hibiscus Coast Traffic

2 Model used data sources such as traffic count data, floating vehicle (journey time) data, traffic signal data, household interview data (in some cases) and manual count data to inform model development and validation. Palmerston North City Council wished to supplement these sources of data with other data sources (using innovative technology) to develop the PNATM. Beca Limited was commissioned to assist with the development of the model and a key first stage in this process was to undertake a scoping exercise to identify available data sources and plan a programme of data collection. Traditionally, the development of a transport model would require $100k to $200k worth of data collection. The techniques used for PNATM were significantly more cost effective. Research has indicated that Bluetooth data collection is approximately ten times cheaper than Automatic Number Plate Recognition (ANPR), which has the capacity to provide similar information on origindestination trip making patterns and journey times. Fleet GPS data is collected all the time by vehicles travelling on the network. This data, when harvested and processed can provide a much more cost effective and higher resolution alternative to road side interview data collection, particularly for commercial vehicles. The Australian Bureau of Infrastructure, Transport and Regional Economics (BITRE) published a study in 2014 (New Traffic Data Sources An Overview). This provides a useful summary of available traditional and more modern sources of traffic data and how they can be used. This states that new data sources are typically more mobile and provide scope for more network coverage and more information about routes and behaviour. As part of this, BITRE produced a summary table shown in Appendix A. distribution modules, followed by peak-hour assignment models for the AM, PM and Interpeak periods. The model has a base year of 2013, with forecast year models developed for 2021, 2031 and The key functional requirements of the model were defined as follows: The model should provide a reliable replication of existing traffic patterns and network performance, suitable to the purpose of the model; The model should relate traffic flows directly to input land use data; The model should provide predictions of changes in traffic flows and patterns in future years, in response to changes in land use or the network; The model should provide strong analysis and graphical output capabilities along with a good GIS interface (for both inputs and outputs); and The model should provide a basis for more detailed models of specific projects. The model covers Palmerston North City and the surrounding hinterland (as shown in Figure 1 below). It covers part of Manawatu District (including Feilding), Tararua District (including Woodville) and Horowhenua District. Figure 1 shows how the geographical coverage has increased over time the initial model only extended to the blue line. It was then extended to the area within the solid red cordon before being expanded further to cover the area within the dashed red cordon. Traffic Model The PNATM has been designed to replace an aging existing traffic model and is a typical 3- stage model developed using the CUBE Voyager software, where traffic demands and flows are derived directly from land use data. The model has 24-hour trip generation and Figure 1 PNATM Modelled Area

3 The study area was broken up into over 200 smaller zones. These zones form the basis of land use forecasting. The road network is represented as a series of nodes (intersections) and links. The model was subject to a robust validation process using standard NZ Transport Agency, IPENZ and Austroads validation criteria. It was found to exceed all appropriate criteria and was therefore deemed fit for purpose. Data Collection A range of data sources were used to create the model. The most up to date census data was collected in 2013 and has represented a key input to the model. The census data used includes population, household and employment data. School roll data was sourced from the schools directory on the Ministry of Education website and tertiary roll information was sourced directly from PNCC and Massey University. The key source of origin-destination data was the census Journey to work (JTW) data. Statistics New Zealand typically supplies this at census area unit (CAU) level. In order to inform the model, this was further disaggregated to mesh-block level from the population and employment data, then aggregated to model zones. Although a very good source of travel data, this only applies to the commuter trip. The model networks have been developed from a range of sources, including the previous model, GIS road centreline data, SCATS signal phasing data, site visits and aerial photos. PNCC have an extensive data set of traffic counts, which have been the main source of traffic data for this model build. These have been augmented by similar data from NZTA and the Manawatu District Council. Six sets of traffic counts were used to inform model development, as follows: Counts from PNCC s standard count program Special PNCC counts done to coincide with 2013 census period PNCC special counts on low-volume roads PNC counts focussed on the central Square Manawatu District Council counts NZTA counts Compared to other parts of the country, the area around Palmerston North is very well represented for traffic count data. Therefore, it was not considered necessary to collect any additional traffic counts, with the exception of a special count site at Palmerston North Airport. This data was processed to identify sites with missing data or obvious errors, or undertaken in December or January months. These checks included: Gap check: All weekday counts should be similar in magnitude. This check eliminated incomplete counts. Also numbers of blank or zero were checked in the raw data. Flow balance check: AM and PM peak opposite direction flows should be balanced. Likewise IP both directions should be balanced too. HCV % check. Percentage of HCV should be similar for both directions. If there was a discrepancy, a check was undertaken with adjacent count sites. All counts were loaded into the model and checked against flows from the initial model runs. Turn counts at intersections do not generally exist, however the model used targeted turn data from the SCATS (traffic signals) system. This data was used with caution as SCATS detector data is not always reliable for counting vehicle flows, especially on movements without detectors or shared detectors. PNCC have a comprehensive set of travel times collected through floating car surveys, which formed the main dataset for validating travel times. With recent developments in computational power, a range of new data sources were used to develop the PNATM. It is the first

4 model of its kind to be developed using commercial GPS and Bluetooth vehicle tracking data. Commercial GPS data sourced from electronic road user charges (ERUC) data has been used to estimate truck demand matrices. Vehicles that pay RUC electronically are fitted with a GPS device that monitors the location of the vehicle on the network approximately every 200m. Although some light (diesel) vehicles are captured in this data, it is predominantly sourced from trucks (approximately 40% of heavy vehicle RUC is captured through the ERUC system). real time, anonymise that device s unique address. Data collected by for the NZ Transport Agency has demonstrated that the Blip Track system harvesting data from Bluetooth devices alone tracks around one in six vehicles. Figure 2 shows the location of the Bluetooth devices installed for this study. The detectors were deployed on Sunday 6 th April 2014 and collected for the whole of the following working week. Only vehicles that were detected at a minimum of two Blip Track locations were included in the survey. Project specific origin-destination surveys were not undertaken for this study. The model generates synthetic matrices from trip generation and distribution modules so it does not need the survey data to build the matrices. One gap identified during the model scoping was a lack of data relating to through movement (external to external). To gather through traffic data a network of eight wireless journey detectors was established on key routes into and out of the city. Wireless journey monitoring relies on a suite of permanent or temporary Bluetooth roadside detectors that identify the unique identifying numbers of passing Bluetooth devices and are able to match these as the devices are detected along a road network. The detectors that were deployed for this study are able to capture not just Point to Point travel times but also gather data of sufficient quality and quantity to enable the strategic analysis of route choice between multiple detectors, hourly speed profiles and pseudo origin/destination within the survey area to be analysed. The Blip Track detectors used for PNATM have been supplied to Auckland Transport, the New Zealand Transport Agency and Christchurch City Council for several years. The Blip Track system detects in-vehicle Bluetooth devices such as phones, cars, earpieces, sat-navs and tablets. As the devices pass the detectors they detect and, in Figure 2 Location of the Bluetooth devices Between each node combination the number of through journeys was recorded. In total over 15,000 through journeys were recorded during this five day survey. The data was processed to produce normalised daily flows for each of the four daytime periods; the proven Blip Track Sample Rate of 17% was used to extrapolate the sample size to an estimated total vehicle flow for each period (a 17% mean sample rate for Blip Track has been measured by comparing the matched Blip Track detections between two adjacent detectors). A localised correlation of the Blip Track was calculated to be 15.3%. This was calculated by determining the mean daily count (488) of vehicles recorded between node 1 and 7 -

5 where there are limited opportunities to join and leave SH56 - and the 2009 AADT of 4488 on that stretch of SH56. This 15.3% Sample Rate is considered to be within the expected fluctuation and margin of error caused by using an AADT value rather than a contemporary vehicle count. As with any survey there are several anomalous results however the quality and diagonal symmetry of the data, together with spot checks against anticipated flows does support the reliability of the data presented. In addition to node to node trips data was collected on node to area trips. Utilising the data that was collected by the Blip Track system, it was generally possible to set up logical tests to identify with reasonable certainty that vehicles that were detected passing a node are likely to have finished their journey in one of three areas: Area A City Area B - Massey University Area C Area surrounding Linton Army Camp This was used to provide further certainty to the modelling undertaken. Asset Management Planning Palmerston North City Council collects a range of different traffic data via traditional collection methods for the purposes of managing day to day network operations enabling long term network planning and optimisation of maintenance and renewal activities. The types of traffic data collected to inform these decisions are more often than not a snap shot of the network operating conditions taken at several predetermined normal times of the year. Historically Council has considered these methods as an acceptable balance between cost, data collection and accuracy. In 2014 the Council revised its asset management plan and many of these traditional collection methods were used to formulate Lifecycle Management Strategies for its assets. These strategies detailed the operational, renewal and capital work categories for assets. Although a considerable amount of data for the new transport model comes from traditional collection methods, its utilisation of both Fleet GPS and Bluetooth adds a further level of information used to predict future growth and demand on the network. Fleet GPS data provides insight into the routes taken by heavy commercial vehicles. This can be used to help formulate pavement maintenance regimes and to understand which structures are likely to be put under strain by high levels of heavy vehicle usage. This predicted future demand is being fed back into the lifecycle management strategies for the network ensuring correct decisions are taken with respect to the operational and renewal of existing assets along with the future investment in capital works. The Council is currently in the process of categorising its road network based on the functions they perform and in line with the requirements of NZTA s One Network Road Classification (ONRC). This classification will help the Council to plan, maintain and operate its road network in a more strategic and consistent way as well as obtaining the necessary funding where required. An Integrated Transport Strategy is also being developed for the city bringing together land use planning, transport planning, road infrastructure programming, and urban design considerations with its purpose to achieve a transportation system that balances everyone s needs and expectations. Both of these tools are designed to manage future demand through the application of strategies but will rely heavily on vast amounts of data collection initially through traditional means but moving steadily towards more modern and advanced methods such as Bluetooth and WiFi. The use of Fleet GPS data will become especially important with the City positioning itself as a logistics hub for the lower North Island. This data will not only provide the Council with a powerful tool to aid

6 funding cases for capital works but also strengthen its asset management abilities in both operational and renewal works. Food HQ The Council has recently commissioned a large transport study on the effects of the Food HQ development in the southern part of the city. Food HQ is research collaboration between Massey University and the Fitzherbert Science Park forming a fully integrated super campus within the City. The basis of the study is a wireless journey monitoring system using both Bluetooth and Wi-Fi collection methods. The study aims to develop a detailed understanding of transport, cyclists and pedestrian movements around the development now and in the future. Strategic intelligence from the wireless journey monitoring system will be available in a variety of formats, these being: Traffic speeds between adjacent detectors are displayed as colour coded speed maps; these are available for playback to assist in understanding speed patterns around the area The performance of sections of routes providing KPI data on the percentage of journeys completed at set speeds Origin / Destination analysis around the area; identifying where vehicles (or people/cyclists) join a route and where they leave it The new traffic model, coupled with the data collected and demand changes associated with the Food HQ development will be used to assess the likely changes in travel patterns. This will be used to evaluate how the concepts are likely to perform and identify potential areas for improvement. The model will be tested for 2021, 2031 and 2041 future years taking into account network and landuse changes taking place elsewhere in the Palmerston North area (including the changes at the nearby Linton army camp). This evaluation will be undertaken from a multi modal perspective cognisant of the proposed road hierarchy. Signalised Intersection Turning Movement Data The Council commissioned a survey to gather intersection performance data on 12 signalised intersections in late 2014 using Bluetooth detection. The aim was to capture data rich information on vehicle movements, flows, transit times and variability in transit times at these intersections. Over 32,000 separate vehicle movements were recorded over a 3 week period with the data provided to the Council in the form of a spreadsheet. The data is able to summarise each of the intersections performance along with graphs to indicate each possible vehicle movement at each intersection and have data on sample sizes by hour and also a profile of the variability of transit time during the selected period. This survey was intended to assist with intersection optimisation and should any intersections be modified, either with physical roading changes or through changes to traffic signal programming it will be possible to repeat the survey to provide a quantitative indication of any change. Using this data it should also be possible to assess the Level of Service of each intersection. Summary and Conclusion Recent and emerging technologies offer significant opportunities for collecting more information, more cost effectively, about personal travel activity and road use, that can better inform day to day network management, long term infrastructure planning and road user travel choices. The Council invests significant resources per year into collecting road traffic data using conventional means. The availability of more detailed information through more advanced collection techniques could potentially improve network management and long term planning as well as having cost saving implications to the already stretched budgets. The use of Bluetooth and WiFi technologies alongside Fleet GPS has given Palmerston North City Council a range of new possibilities to collect reliable traffic data, to

7 analyse traffic behaviour and to optimise the existing infrastructure for better traffic flow. It offers some significant advantages to the conventional collection methods such as, no roadside maintenance, easy configuration and calibration, fast and inexpensive installation. Data is also collected in real time and stored for historical analysis and reporting. The wealth of data now available is proving to be a significant asset to Council and informs a range of planning, design and management processes. Author Biographies Simon Cager is a Project Manager with a broad range of experience within the highway sector. He is currently part of the City Networks Roading Team within Palmerston North City Council and responsible for ensuring that Palmerston North s roading infrastructure is able to meet the demands of the city now and into the future through the investigation and development of potential capital development projects. He has worked on a variety projects both in the UK and New Zealand within various engineering consultancies and local government. Contact : Palmerston North City Council, Private Bag 11034, Palmerston North simon.cager@pncc.govt.nz Alan Kerr is a technical director in Beca s transportation team. His expertise is in transport data analysis and modelling as well as crowd and special event planning, but he is also experienced in many broader transport planning areas such as demand forecasting, congestion charging appraisal, accessibility and mobility planning, development planning and journey time variability research. Alan led the team developing the PNATM for Palmerston North City Council. Contact : Beca, Aorangi House, 85, Molesworth Street, Wellington 6011, PO Box 3942, Wellington alan.kerr@beca.com

8 Appendix A Summary Table of Data Collection Technologies (Source: BITRE)