From 20 to 24 June 2016, I had the opportunity to participate in the urban modelling workshop organised by the ESRC Strategic Network: Data and Cities as Complex Adaptive Systems (DACAS). The workshop was held in the ICTP-South American Institute for Fundamental Research in the municipality of Sao Paulo, Brazil. The event brought together researchers across multi-disciplinary fields, all interested on how Data and Complex Adaptive Systems can be applied to describe and understand the underlying emergent behaviours in cities, and ultimately, plan for smarter cities: sustainable and resilient.
Data and urban challenges
On the opening day, Tomás Wissenbach from the Sao Paulo’s urban development agency talked about the challenges of urban transformation in Brazil and explained the recent efforts of Sao Paulo’s administration to collect all available datasets across the different governmental authorities regarding Sao Paulo’s population and infrastructure. This data fusion and processing endeavour culminated in an online interactive application (Figure 1) which anyone can access and download the datasets. In the second phase of the project, Wissenbach announced the possibility to collaborate in projects that can capture the urban transformation experienced in the city, and that can help the government to make informed decisions to plan for a better city.
Figure 1. Screenshot of interactive Map of Sao Paulo. Blue for the metro lines, red for the bicycle lanes, and in orange tones the population density
Prof. Ana Bazzan, from the Institute of Informatics at Universidade Federal do Rio Grande do Sul (UFRGS), presented in her keynote presentations on her work in agents and multi-agent systems in traffic and transportation. (video here, slides here) The talk started with the rise of the cities, and the inherent transportation challenges within. Prof. Bazzan introduced then the idea of a data ecosystem triggered by people’s participatory sensing as the key to develop analytical applications to improve the transportation system. In a smart city, citizens interact directly with the system instead of just being passively receiving information. This change in the paradigm requires a human/agent- approach for the information, modelling and control challenges in which humans act as both targets and active subjects (i.e. sensors).
Putting all together: Data and Complex Adaptive Systems for Transportation Planning
My presentation on our research project Engaging Big Data supplemented the prior presentations quite nicely. This ongoing project conducted at the Future Cities Laboratory of the Singapore ETH Centre seeks to build up an agent-based simulation framework for transport planning using MATSim that can benefit from both urban mobility sensors (e.g. mobile phone and smart card data) and traditional data inputs (e.g. household travel survey and census information) (Figure 2). In the era of ubiquitous sensing and big data, the first challenge for developing the next generation of predictive, large-scale transport simulation models relies on designing a data mining pipeline that can fuse the knowledge from these different datasets in order to have an enriched and full explanation of the urban mobility dynamics. The second challenge aims in using this information to automate the parameters of a MATSim scenario, which would not only allow to significantly lower the efforts required for setting up simulation scenarios but would also lead to even more realistic results. This will ultimately serve as a platform to test the viability of policy and infrastructure decisions before they are implemented, and guide and inform the urban and transport planning process.
Besides the workshop, I took the opportunity to experience some of the results of the city of Sao Paulo’s recent pushes to improve the adoption of sustainable transportation policies. Those initiatives primarily target the notorious traffic congestion the 21 million inhabitants of the metropolitan areas are suffering from. With the introduction of the ‘Bilhete Único’ in 2004, a smart card automatic fare collection system for the public transport, citizens are being incentivise to opt for public transport through standard fares regardless of distance or number of connections. The data on mobility patterns that this system generates every day would also be an ideal source for setting up Big Data driven urban transport simulation. In addition, Sao Paulo’s municipality has recently done major investments on bicycle infrastructure throughout the main avenues of the city, including the symbolic, Avenida Paulista. (Figure 3)
– although my colleagues at FCL who study how street design can support active mobility think that there is potential to make cyclists feel more comfortable and safe on this major arteria ;-).
Every year, on 2nd August many transport researchers from around the work feel utterly relieved after they successfully submitted their papers for presentation at the Annual Meeting of Transportation Research Board. The meeting, which actually is the by far biggest conference in our field takes place every year in Washington D.C. in January of the following year.
Sometimes I ask myself what’s to point of going to conference in an age where researchers are not even present one but often even several social networks that are purely dedicated to scientists and constant bombardment of Tweets, Facebook updates and new blogposts ;-).
But being at TRB is always special to me. Not only it is great to catch up with colleagues in persons to informally exchange and spin new ideas, there are also always those chance acquaintanceships that make personal and research life so much richer. And checking out the mood of a city just before a new president (blonde for sure, but hopefully not male) is inaugurated is also always special.
Enough small talk, here come in an exclusive sneak peek the three submissions from people related to the Engaging Mobility group at the Future Cities Laboratory of the Singapore ETH Centre. My great co-authors and are looking forward to hopefully positive constructive points for critique from the reviewers, but also are curious on your comments!
In this paper we elaborate on potential use cases of Virtual Reality (VR) in transportation research and planning and present how we integrated procedural 3D modelling and traffic micro-simulation with the rendering capabilities of a game engine in a semi-automated pipeline.
Through a review of potential practical applications, we present how this pipeline will be employed to distil behavioural evidence that can guide planners through dilemmas when designing future cycling infrastructure. At the same time, we are studying efficacy of VR as a method for assessing perceptual behaviour as opposed to traditional methods of visualization. Concretely, we present how the pipeline can be adapted i) to generate parameterised visualisations for stated preference surveys, ii) as a platform for a cycling simulator and iii) to communicate different design scenarios for stakeholder engagement. The flexibility of procedural programming allows discretionary changes to the street design and the traffic parameters. Through this experience of developing procedural models, traffic microsimulations and ultimately VR models for streets in Singapore, we find that visual and temporal feedback enabled by VR makes several important design parameters observable and allows researchers to conduct new types of behavioural surveys to understand how people will respond to different design options. In addition, we conclude that such VR applications open new avenues for citizen engagement and communication of urban plans to stakeholders.
Virtual Reality software pipeline to integrate CityEngine and Vissim output in Unity3d
The indices for walkability proposed so far are mostly ad-hoc and refer generally to the closest amenities/public transport stops and the existing network structure. They are ad-hoc as the weights of the attributes are generally arbitrary and do not reflect the independently measured preferences of the users and residents. Furthermore, they do not include design attributes such as the location of crossings and walkway design features, which are very relevant for actual planning decisions.
In this paper, we propose a walkability index that can be behaviorally calibrated and has been implemented as a GIS tool and is published as Open Source software. The Pedestrian Accessibility Tool allows evaluating existing and future urban plans with regards to walkability. It calculates Hansen-based accessibility indicators based on customizable specification of generalized walking cost and user-defined weights of destination attractiveness.
Comparison of walksheds with Pedestrian Accessibility Tool: impact of replacing a pedestrian overhead bridge with a conventional zebra crossing. Click the image to start the animation!
Given the rapid technological advances in developing autonomous vehicles (AV), the key question appears not so much anymore how, but when AVs would be ready to be commercially introduced. Therefore, it is very timely to explore how the new way of travelling will shape the traffic environment in the future. Questions regarding the environmental impact, changes in infrastructure and policy measures are widely discussed. Most likely, the introduction of AVs will not only add an option to the traveller’s choice of means of transport, but also shape how people interact with the traffic environment. From a transport planning point of view, key questions concerning the introduction of AVs as a new means of transport are how it will influence travel behaviour, how supply and demand for AV will balance, how it impacts the viability of existing public transport services and how AVs will impact congestion and demand for parking.
In this report, a new simulation framework based on MATSim is presented, allowing for the simulation of AVs in an integrated, network- and population-based traffic environment. The demand evolves dynamically from the traffic situation rather than being a static constraint as in numerous previous studies. This allows for the testing of various scenarios and concepts around the introduction of AVs while taking into account their feedback on the travellers’ choices and perceptions.
Using a realistic test scenario, it is shown that even under conservative pricing a large share of travellers is attracted by autonomous vehicles, though it is highly dependend on the provided fleet size. For sufficiently large supplies it has been found that for the autonomous single-passenger taxis in this report the vehicle miles travelled increase up to 60%.
Share of the available travel modes in a percentage of the total number of trips, dependent on the number of available AVs.
Experiences from a pedestrian counting experiment with Placemeter
As soon as a pedestrian or cycling planning project appears in the pipeline, you have to think about counting methods. Knowledge of flows and densities is essential for an intelligent and safe infrastructure design and before and after evaluations. The traditional method to perform such evaluation is to conduct manual counts: a labour intensive and therefore costly endeavour. Emerging technologies promise to cut down on counting cost. The folks at Alta Planning did a great job and published a White Paper which covers key emerging technologies in addition to the existing NCHRP 797 guidebook on pedestrian and bicycle volume data collection.
For our ongoing research project Engaging Active Mobility at the Future Cities Laboratory in Singapore, we are interested in testing the viability of some of those new technologies to count pedestrians and cyclists. Four our first test, we used sensor products of Placemeter, a startup founded in 2012 in New York. Placemeter positions itself as an ‘urban intelligence platform’, which ‘ingests any kind of video to analyse pedestrian and vehicular movement, revealing hidden patterns and strategic opportunities’, to help ‘build stronger businesses, efficient cities, and innovative neighbourhoods worldwide.’
Placemeter’s counting technology
Placemeter currently supports two types of counting devices: the Placemeter Sensor, which contains a camera and on-board processing unit, and an off-the-shelf IP camera (see Figure 1). In the case of the Placemeter Sensor, the video data is directly processed in the sensor, while the video stream of the IP camera is broadcasted to the Placemeter servers which run the algorithms that extract the count information. In both cases, the algorithms are able to identify counts by the direction of movement across used-defined measurement points. However, currently the Placemeter products do not allow to differentiate whether a pedestrian, cyclist and motorized vehicle crossed a measurement line.
Figure 1: Placemeter Sensor (left) and IP camera (right)
Installation Set-up
We ordered both type of Placemeter Sensor products in October 2015 at the price of USD 90$ each. Delivery to Singapore was another 30$ each. While Placemeter promises that measuring with your very first sensor will be free forever, for any additional sensor they charge 100$ per measurement point and month, but allow you free counting during the first month.
The Placemeter Sensor arrived within a few days in beautifully custom-designed cardboard box while the IP camera by DLink got shipped in the standard wrapping.
The technical equipment is principally very easy to install. Nevertheless, some issues appeared when connecting the Placemeter Sensor to our WLAN. This step is necessary to connect the sensor to to Placemeter’s servers and to specify the measurement points through Placemeter’s web-based interface. We then tried to narrow down the problem by setting it up within other WLANs, but without success. Raising the issue to the helpful Placemeter customer care, they offered to replace the sensor at free cost and to cover any shipping expenses.
The setup with the replacement sensor was then straightforward and did not take more than 10 minutes. After connecting, the live stream of the sensor is displayed online and user-defined measurement points can be drawn as a line direct on the picture.
Besides access to WLAN, both the IP camera and Placemeter Sensor require electric power supply. The enclosed power cable for the Placemeter Sensor is about 5m long, the standard cable delivered with the DLink IP camera is about 2m. While you obviously always can use a cord extension to extend your setup range, it also means that you need to have access to a power outlet near your count location.
First results
IP Camera
While the Placemeter Sensor was being replaced, we started to install our IP camera and decided to place it in front of our main meeting room, the ValueLab. Figure 2 shows the indoor setting and the three measurement points (screenlines) placed in order to count people coming from left and right and entering the meeting room. We can see that our office is brightly lit with artificial light, but also bright daylight as is typical for equatorial regions. However, the combination of the dark floor, white walls and bright windows plus some obstructing furniture create a situation that is characterised by high contrasts.
Figure 2: Screen shot of the IP camera live stream and fixation on the ceiling
The ValueLab, also serves as a venue for public lectures. These lectures offer the perfect opportunity to conduct manual counting and compare the results with the counts produced by Placemeter. Figure 3 shows the number of people counted at the door (horizontal screenline) with the IP camera as well as the manual counts. In order to simplify the manual counting, we simply counted the number of people who attended a lecture. By multiplying this number by a factor of two, we obtain a minimum value of the number of people who traversed the measuring point at some point during the measurement period.
In other words, the real value of the manual counts should be higher, taking into consideration that some people would enter and exit the room few times (e. g. to take a phone call or so). Nevertheless, in almost all cases the manually counted ‘minimum value’ was higher than the amount of participants recorded by the IP camera.
Another test was to compare the monthly data obtained from Placemeter for the IP camera. Since the ValueLab has a single entry point, we just compared the number of persons entering and leaving the room. During the period of 30 days, the IP camera counts in average 13% more people entering than leaving the room. We think this is because the arrival process is different from the departure process. People trickle in before a lecture, and rush out simultaneously to grab a coffee, challenging the Placemeter counting logarithms.
We also suppose that the IP camera has problems to count pedestrian just before or after a door, since in this case the camera angle is reduced and only one side is visible.
Placemeter Sensor
Not being truly convinced by the counting performance using the IP camera, we also tested the newly arrived replacement Placemeter Sensor in this challenging indoor setting. However, results showed similarly inconsistent patterns. Apparently, the the high contrast setting is beyond the operational limits of Placemeter’s video processing count algorithms.
Disappointed to not being able to automatically count the popularity of our main meeting room, but not disheartened with the potential of the technology altogether, we set out to give the Placemeter Sensor a second chance in a more conducive environment. The setting for the second experiment is the entrance to one of the CREATE buildings. The video frame is characterised by a rather homogenous contrast as shown in Figure 4.
Figure 4: Screen shot of the Placemeter Sensor live stream and list of measurement points
The measurement points (which actually are lines) were drawn to form a measurement square which means that people entering the square should exit it again within a few seconds. This allow us to do a quick accuracy test of the Placemeter Sensor.
Figure 5 shows an overview of the manual count results and the data delivered by the Placemeter Sensor. Different to the outcome of the first experiment, the manual counts are exact (no minimum values). Manual counting was performed for the duration of one hour at three different days. Except for the measurement points ‘Door_Create_In’ and ‘Door_Create_Out’ the results match pretty well with the Placemeter Sensor outputs.
In general, the results from the horizontal measurement points are less accurate then the data from vertical ones. Thus, we suppose that the video processing algorithms can recognise pedestrians better on vertical measurement points, not least because of a favourable angle between pedestrian flow and measurement point.
Figure 5: Results of manual and automated counts on an outdoor setting
Similar to the IP camera, we made also in this case a second sanity check by comparing the number of people entering and leaving the area between the four measurement lines; over a period of two weeks, the Placemeter Sensor counted in average 10% more people entering this area than leaving this area. Apparently, we have some sort of Bermuda square here 😉 !
Conclusion
In general, with both Placemeter Sensor and IP camera, the detected pedestrian count is lower than the actual count. With our small sample, we made the following observations:
Both the IP camera and Placemeter Sensor seem to have problems to deal with groups of persons.
Especially in the cases where people are walking through a door, the sensors seem to be less accurate than manual counting.
The accuracy of vertical screenlines is better than horizontal measurements screenlines.
Likewise, both sensors have problems in indoor settings consisting of furniture, high colour contrasts and changing lighting conditions.
Even though we see a demonstrable utility in the conceptual idea of Placemeter, we learned that the field of application of Placemeter Sensors seems to be limited by the visual setting and the requirement of having access to a power outlet and a WLAN. For peak hour pedestrian flows and public transport measurements, a shorter time interval might be necessary. However, if the situation where you want to count fulfils those criteria, you should definitely consider Placemeter’s products for your counting project as the setup is straightforward and data can be easily collected for long periods of time at marginal additional cost.
