As we know, urban planning concerns itself with the design and regulation of the uses of space in the urban environment, and on the location of human activities within it. Regarding existing urban settings, a key concern is understanding how the space is currently used, and how it would be affected by change, both from redesign, or simply from increased use over time.
This story, shared by Senior Transport Consultant Francesco Angelelli, highlights how Atkins, a leading international consulting company, used an innovative approach leveraging the latest technologies to study human behavior and use patterns at an iconic place: Oxford Street in the City of Westminster, one of the most famous destinations in London.
The Urban Setting and its Challenges
Originally part of the Via Trinobantina, a Roman road between Essex and Hampshire, Oxford Street has existed for over 2,000 years. It became Oxford Street in the 18th century, when it began to change from residential to commercial. Today, it is Europe’s busiest shopping street, with half a million daily visitors.
Because of its popularity among tourists and shoppers, the street’s capacity is under increasing pressure. Businesses compete to secure a high-profile presence in the area. As a result, there are growing concerns about traffic pollution, general accessibility and pedestrian safety. In addition, the opening of the Elizabeth Line, the railway route built by Crossrail, under construction since 2009, due to open in December 2018, is expected to increase footfall and exacerbate existing problems. All of this has prompted a much needed discussion about the need to reshape the area’s public use.
The Crown Estate appointed a multi-disciplinary team to develop proposals for Oxford Circus and its surrounding streets. Atkins’ pedestrian modeling team was involved in the proposal’s assessment to accommodate the increased demand, improving accessibility while providing visitors with a world-class experience.
Key project challenges were the size of the area to be assessed and the large number of pedestrians involved. Developing a model capable of simulating existing conditions in such an environment demanded innovative data collection methods supported by traditional survey data. After evaluating several survey technologies, the Atkins team opted for WiFi Survey: monitoring the movement of active WiFi- enabled devices throughout the study area. WiFi survey provides relatively clean samples with high sample rates. In addition, it enables relatively straightforward long-term comparison between different monitored areas, which would be difficult, if not impossible using traditional sampling methods.
Following their standard approach, the Atkins team developed a base-year model which simulates existing conditions to a reasonable level of validation. The methodology has been successful in the calibration of the base model (R-Squared of above 0.96 and GEH < 5 for most measured flows). They used this as a basis for the development of future year scenarios for the assessment of design alternatives. The 2018 demand level has also been increased to develop 2019 models.
All models were rendered in a 3D Virtual Reality environment, so the team could literally experience walking through the base model. This proved to be an incredibly useful aid in refining and visually validating modeling assumptions, thus solving a common problem in the pedestrian modeling sector, as standard software only allows modelers to visualize simulated crowds from a distance. The 3D Virtual Reality environment also enabled exploring spatial improvements with the design team in a seamless and highly effective manner.
The Atkins team deployed 21 WiFi devices (“nodes”) on building cornices. The nodes were attached to the building’s floodlight mains and used 3G/4G connections to stream data in real-time. Behind the scenes, Accuware’s WiFi Location Monitor system determined the approximate location of all WiFi devices by triangulating their positions based on their signal strength.
Weekday profiles support peak-period assumptions
One day, August 11th at 9 AM, the number of devices detected peaked and then suddenly dropped. As this trend was very unusual for this time of the day, the team worried that the system was experiencing technical problems. But just then they learned that Oxford Circus station had been evacuated because of a train on fire. With the station temporarily out of service, the overall population in the area was lower than usual. This incident revealed that the data captured will be very useful for planning emergency operations.
O/D matrices for weekdays and weekend
As the datasets were growing week by week, the team began analyzing the data and obtaining useful insights both for modeling and for stakeholder information. For example, the combination of Origin-Destination (O/D) matrices and daily profiles for both weekdays and weekends enabled them to define different areas by showing which were commuter-driven, leisure-driven, and neutral; where neutral is the configuration that maximizes public space usage throughout the week, an important indicator of the quality of public spaces.
Pedestrian speeds on Regent St and Argyll St
The analysis covered sample speeds on Argyll Street, which is a pedestrian lane, and Regent St which is not. As the speed profiles are similar it is clear that the data was not influenced by vehicular traffic. However, for some devices the detected distance is less than the actual one due to low reporting frequency. The team expected that the resulting average speed would be overestimated. To eliminate this effect, they did not consider higher (and implausible) values. When the tails were excluded, the profiles show a normal distribution with an average speed of 1.2 meters/sec.
Rendering pedestrian simulation with Virtual Reality
Behind the scenes
To implement their solution, the Atkins team deployed Accuware’s WiFi Location Monitor, a system designed to passively detect and locate active WiFi devices, such as cell phones and tablets.
Using WiFi Location Monitor requires deploying WiFi “nodes“, which are WiFi routers set on listening mode. Nodes detect the presence and signal strength of active WiFi devices nearby, uploading their data to a cloud-based server. The server estimates the location of each WiFi device from the signals collected by multiple nodes. Each device is identified by its WiFi MAC address. Note that no personally identifiable information is ever collected. All collected data is truly anonymous. Actual location of each device is estimated within a 3-meter radius of its actual location. which is ideal for urban use surveys.