Bike to the Future

Experiencing alternative street design options

We believe that Virtual Reality (VR) offers tremendous opportunities as people can explore the future design options from different perspectives in the Virtual World. In the case of street design, for example, one can explore the experience from the view angles of motorists, cyclists, pedestrians and even children.

VR also offers new opportunities for stakeholder engagement as participants can provide valuable feedback to planners how to improve the design before it is actually built.

Fig 1: The Bike to the Future exhibit combines the latest technologies in 3d modelling, traffic simulation and game development to create a realistic Virtual Reality experience
Fig 1: For the Bike to the Future exhibit, we combine the latest technologies in 3d modelling, traffic simulation and game development to create a realistic Virtual Reality experience

Exploring Virtual Reality as a Planning Tool

We all are very familiar with the maps and renderings planners and designers use to communicate development plans. But imagine if you could explore future planning scenarios in Virtual Reality!

At the occasion of this year’s Park(ing) Day in Singapore, the Future Cities Laboratory will set up a digital peephole into the future to test new possibilities in engaging people for street design and urban development projects. The technology combines the latest 3D modelling and traffic simulation techniques in Virtual Reality to showcase how streets can be re-designed to make cycling and walking a more pleasant experience

Fig 2: Alternative street designs for Seng Poh Road and Lim Liak Street, 3d building data © Urban Redevelopment Authority
Fig 2: Alternative street designs for Seng Poh Road and Lim Liak Street, 3d building data © Urban Redevelopment Authority

Join us in Tiong Bahru on Friday, 16 September 2016

Visitors are invited to cycle in Virtual Reality through three local streets: Lim Liak Street – Kim Cheng Street and Seng Poh Road. Each street features a particular re-design to make cycling and walking more attractive. After the virtual ride, a short survey will be conducted to better understand how Virtual Reality applications can help planners getting feedback from local stakeholders: What are your needs? What do you like in the new design? Which design elements could be adapted or improved?

We are looking forward to welcome you at our parking lot on the 16th of September from 9am to 8pm! All visitors taking the survey do not only take part in a lucky draw with exiting give-aways, but also will have for the first time in Singapore the chance to test both the latest Virtual Reality goggles HTC Vive and Oculus Rift.

Date:  Friday, 16 September | Time: 09:00 to 20:00 |   Where: Lim Liak Street (Opposite Tiong Bahru Market)

We would like to thank the Singapore Urban Redevelopment Authority for making the highly detailed 3d model available for this research project.

The fruits of this year’s TRB submission frenzy

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!

Visualizing Transport Futures: the potential of integrating procedural 3d modelling and traffic micro-simulation in Virtual Reality applications

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
Virtual Reality software pipeline to integrate CityEngine and Vissim output in Unity3d

Introducing the Pedestrian Accessibility Tool (PAT): open source GIS-based walkability analysis

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.
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!

Simulation of autonomous taxis in a multi-modal traffic scenario with dynamic demand

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.
Share of the available travel modes in a percentage of the total number of
trips, dependent on the number of available AVs.

Gussy up Vissim with the rendering power of a game engine.

Why does traffic microsimulations need a facelift?

The traffic microsimulation tool Vissim is one of the most advanced traffic simulation software. Vissim offers great possibilities to reproduce extensive urban traffic situations including public transport, individual cars, trucks, bicycles and of course, pedestrians.

The software is mainly purposed to quantitatively evaluate traffic scenarios with regards to vehicle and pedestrian densities, road and intersection capacities, as well as travel times or delays. But traffic simulations are also instrumental to illustrate possible design scenarios in pictures or videos to decision makers and the general public.

However, while the 3D visualisation capabilities of microsimulation tools such as Vissim have considerably improved over the last few years, there are still limitation when it comes to the realistic rendering of 3D environments (Figure 1).

Figure 1: examples of simple 3D street views rendered in Vissim
Figure 1: examples of simple 3D street views rendered in Vissim

The roads and objects usually can’t be represented by varying surfaces and the animated models of pedestrians and cyclists are quite clumsy. While such restrictions are of minor importance in conventional applications, the 3D display options are insufficient if we aim for creating Virtual Reality environments. For this reasons, we decided to combine the strengths of Vissim in simulating complex traffic interactions, with the strengths of a 3D rendering engine: Unity.

Our pipeline so far

The logic behind the pipeline is quite straightforward: from Vissim, we only need to export the trajectories of the simulated interactions between pedestrians, bicycles and cars and the commands related to the traffic lights, but use Unity to animate the respective 3D models. The export functions of Vissim allows to do so. Indeed, the coordinates of pedestrians are written in a ‘.pp’ file, while bicycles and cars are saved in an ‘.fzp’ file. The traffic light programme is written in a simple XML-file, and therefore easily exportable.

Basically, the files with the pedestrian and vehicle trajectories are simple CSV (Comma Separated Values) text files and can be read in Unity with appropriate scripts. The information exchanged is: simulation second, number of pedestrian/vehicle, type of pedestrian/vehicle, x-, y- and z-coordinates. For the vehicle, two coordinates (front and rear) are required in order to extract the size of the object.

The creation of these files in Vissim is simple. Before to run the simulation, the option ‘evaluation’ in the toolbar should be selected and, after some settings (see Figure 2), the text files will automatically be created. To ensure that file sizes remain easy to handle, we restricted to a time resolution of 4 simulation steps per second, but included an interpolation feature in our Unity import-scripts. Otherwise, the text files will grow to some billion lines, depending on the number of pedestrians and vehicles simulated in the network.

Figure 2: Settings needed in order to create text files with pedestrian trajectories
Figure 2: Settings needed in order to create text files with pedestrian trajectories

Apart from the trajectories, the files related to the traffic lights should also be exported (‘.sig’ files). There, we also needed an appropriate script to read the times and the colours of the traffic light and represent it correctly on a new 3D model with different street lights in Unity.

Great possibilities with Unity

There are many great 3D engines available out there, but we chose Unity because of its fantastic visual capabilities and Virtual Reality features, ample range of file formats and ease of use.

Some of the more noticeable visual improvements when going from Vissim to Unity are:

  • Physically based lighting and shadowing
  • Global illumination
  • Reflections
  • More realistic skies
  • Better texture filtering and antialiasing
  • Post-process effects (Depth of field, motion blur, bloom, etc.)

Other more subtle details, for which we had to write scripts, also contribute to a more realistic experience:

  • Rotation of vehicle wheels according to their speed
  • Vehicle brake and turn lights
  • Interpolation of vehicle, cyclist and pedestrian movements for a smoother animation.
  • Speed-controlled walk animation for pedestrians

Unity with its tons of features allowed us to create various types of content such as scripted screenshots, videos and 360 videos. The making of videos was possible thanks to its animation capabilities, which we used to create a few camera animations to glide through our 3D environments.

Figure 3: graphical user interface of Unity3d
Figure 3: graphical user interface of Unity3d

Some of the scripts (which are available in our GitHub repository) we wrote in Unity weren’t dedicated to the final visual appearance, but still they played a crucial role in our development. These scripts, for example, helped us to identify issues outside of Unity by visualizing the problem within Unity. E.g. one script generates a traffic movement heat-map, to observe which areas are more/less frequented by simulation agents, or to identify if an agent takes an undesired path which helps to verify the simulation setup (Figure 4).

Figure 4: simulated traces of pedestrians (blue), cyclists (yellow) and vehicles (pink) projected on 3d model
Figure 4: simulated traces of pedestrians (blue), cyclists (yellow) and vehicles (pink) projected on 3d model

Encountered Challenges

When we started importing the first simulation files from Vissim, Unity ran quite slowly due to the large traffic simulation. We finally had to reduce the number of agents in order to keep a real-time interactive experience in Unity.

Since we’re aiming for a Singapore-like environment, we not only need streets and buildings that look like Singapore ones, but we also need a wide range of 3D assets (e.g. road signs, street furniture, buses, cabs, trees) all of which are very specific to Singapore. These are not ready-made assets that can be found for download or sale, but required new 3D modelling efforts.

Aesthetics aside, another associated problem with 3D assets is the quality: too high and it can bring the performance to its knees; too low and it will look horrible.


Unity gives numerous possibilities for exporting visualisations, but of course also to interactively engage with the 3D model in Virtual Reality. For a start, we will use Unity to render out still to be used in stated preference surveys.

Figure 5: prototype renderings from Unity to illustrate different street configurations and traffic  levels to be used in stated preference surveys.
Figure 5: prototype renderings from Unity to illustrate different street configurations and traffic levels to be used in stated preference surveys.

Futhermore, we started to play with 360-degree videos which have great potential to communicate design scenearios to various infrastructure project stakeholders, certainly as you can publish nowadays such videos on Youtube and watch them not only with fancy head mounted displays such as the the HTC Vive and Oculus Rift, but also cheaper options such as the Samsung VR Gear or Google Cardboard.

The bright future of VR in urban and transport planning

We are convinced that the Virtual Reality environments that are based on the integration of an urban environment (CityEngine) and a proper traffic simulation (Vissim) with a game engine like Unity offers new possibilities in terms of communication, visualization and evaluation of planning scenarios. Not least it permits to a non-technical audience to easily immerse themselves in the future environment of proposed project and to have the possibility to compare different scenarios. This allows various stakeholders to provide valuable feedback that can improve the planning process and lead to a better final project before even a single cent is spent in construction and also can avoid expensive alterations works to address usability issues.

Our roadmap

For Vissim, the aim will be to hone the interactions between pedestrians and bicycles, especially in shared space conditions. A realistic representation of these interactions is only possible with extended programming works.

To enhance the virtual reality experience, it is planned to integrate Vissim and Unity using Vissims Driving Simulator and Multiplayer interfaces to allow for so that the traffic simulation can react on user input from the game engine. Reconfiguring a cycling trainer as a controller will allow the user to ride through a virtual environment with 360 visual freedom and interact in real time with the simulation. Colleagues at NHTV Breda have already prototyped such a setup as part of their Cycle SPACES project and Alex already had the opportunity to test it (Figure 7).

Figure 6: Alex testing VR cycling simulator developed by Atlantis Games and NHTV Breda
Figure 6: Alex testing VR cycling simulator developed by Atlantis Games and NHTV Breda

But there is still a lot of things to be improved and we look forward to collaborating with them and Atlantis Games on it and keep you posted on the progress.

How to model pedestrians and cyclists interactions with out-of-the-box features of Vissim?

The growing importance of multimodal traffic simulations

As transport and urban planners around the world consider cycling as an important mode of transport in the urban mobility mix, there is also a growing interest in software tools to support the planning of cycling infrastructure. While macroscopic transport demand models are commonly used to estimate future travel demand and loadings on a network level, microscopic traffic simulation software packages are a widely used for traffic engineering, e.g. to evaluate the performance of different street and intersection designs or traffic signal plans. While those models originally have been developed to model motorised individual traffic and public transport, more recently researchers and practitioners started to adapt those model to also feature active transport modes.

Limits of current microsimulation tools

The current traffic microsimulation software packages such as PTV Vissim, Caliper Transmodeller, Sumo or TSS Aimsun offer great possibilities to model complex but realistic multi-modal traffic situations and some of them also offer remarkable 3d visualisation features to communicate different planning scenarios to a non-technical audience. However, when it comes to the representation of cycling and shared space situations, there are also quite a few limitations.

Heather Twaddle, a researcher at TU Munich an here colleagues recently reviewed existing approaches (Twaddle et al. 2014) for modelling cycling behaviour in traffic microsimulations. While some of the existing software packages allow to model cyclists using the same behaviour models as for motor vehicle traffic, simply adapting the parameters of the vehicle behaviour models to reflect the lower speeds of bicycles does not work for all conditions. Since there exist several unique characteristics that are difficult or impossible to represent with such models, researchers have investigated in adapting car following models, cellular automata and the social force model to better fit the behaviour of cyclist.

However, for our research project that investigates our street design impacts the propensity for cycling, we are also interested in the simulation of mixed traffic conditions on shared spaces or sidewalks that include both space for pedestrians and cyclists.

While simulating two traffic flows (whatever if pedestrians, bicycles or cars) crossing with a sufficient angle can be easily modelled by specifying a conflict area (Figure 1) or a priority rule, the correct representation of interactions lengthwise (with an angle equal to 0 or 180) is very challenging.

Figure 1: 3D view of an intersection between pedestrians and bicycles managed with conflict areas
Figure 1: 3D view of an intersection between pedestrians and bicycles managed with conflict areas

To explore what can already be achieved by adapting the behavioural parameters of Vissim’s standard simulation models, we have done some tests. The basis for all tested scenarios is the same: we want to simulate pedestrians and bicycles sharing a narrow path of about 3m width (about 5m for scenario 3). The number of pedestrians and cyclists is proportional to the lane width and therefore the density is the same in all scenarios.

Pedestrians as small vehicles

In Vissim, there exist the possibility to simulate a pedestrian as a car. The advantage is, that pedestrians are recognized by cars and therefore, interactions are automatically generated. Logically, pedestrians behave also like a vehicle. They follow and overtake each other along defined lanes, which of course is not very realistic.

Figure 2 shows an extreme case, where two links with one lane each are created. If no rules are applied, pedestrians would just walk in middle of the lane, making an overtaking for cyclists difficult. Thus, the driving behaviour should be adapted such as pedestrians prefer to walk on the left. As a result, the observable behaviour resembles to a highway, where slow cars are driving on the sides (pedestrians) and fast cars are overtaking in the middle (bicycles).

Figure 2: Extreme scenario 1 with two links and two lanes (3D perspective of a cyclist)
Figure 2: Extreme scenario 1 with two links and two lanes (3D perspective of a cyclist)

Of course we were not satisfied with this setting. In Scenario 2, we tried to improve the driving behaviours, creating 2 links with 2 lanes each and assigning pedestrians and bicycles randomly to a lane (Figure 3). In addition, a possibility to overtake on the opposite lane was implemented and the width of the lanes reduced to the minimum, just enough that pedestrians and bicycle do not touch each other while overtaking. However, as a result bicycles are jammed behind slower pedestrians very frequently.

Figure 3: Scenario 2 with two links and 4 lanes (3D perspective of a cyclist)
Figure 3: Scenario 2 with two links and 4 lanes (3D perspective of a cyclist)

In Scenario 3 with two links and 6 lanes (3D perspective of a cyclist), the number of lanes were augmented to 6 (3 for each direction) in order to allow more flexibility and, therefore, reduce jam (Figure 4). All in all, this setting creates the most realistic interaction between bicycles and pedestrians if pedestrians are simulated in the car-following model.

Figure 4: Scenario 3 with two links and 6 lanes (3D perspective of a cyclist)
Figure 4: Scenario 3 with two links and 6 lanes (3D perspective of a cyclist)

However, when modelling pedestrians as (tiny) vehicles, there always remains one issue: Because of the infrastructure constraints, the flows are segregated in two directions. This do not correspond to the reality, why we tried another approach.

Having said that, scenario 3 could also be simulated as two 2.5m cycle lanes with free overtaking. The problem in this case is that pedestrians behave very strange and conduct nervous overtaking actions.

Bicycles as big pedestrians

In order to avoid the very mechanical and automated behaviour of pedestrians, we were thinking about inverting the rules: let’s simulate the bicycles as pedestrians!

To this end, the work is done with PTV Viswalk, an add-on module of PTV Vissim. PTV Viswalk is mainly used for modelling railway stations, capacity planning, safety concepts or evacuation analysis and is based on the idea of the social force model.

For this scenario, we just adapted the behavioural parameters of those pedestrians that represent cyclists and used a bicycle 3D model for their visual representation. As Figure 5 shows, the pedestrians are walking more randomly on the assigned surface and there are no clear (directional) flows observable.

Figure 5: Scenario 4 with bicycles simulated as pedestrians (3D perspective of a cyclist)
Figure 5: Scenario 4 with bicycles simulated as pedestrians (3D perspective of a cyclist)

We adapted the behavioural parameters of pedestrians and bicycles as follows:

  • Tau: The greater this value, the lower is the acceleration strength and therefore, the time to reach the desired speed is increased. Tau should be lower for pedestrians than for cyclists.
  • React to n: It is important that the number of considered n is low for bicycles. This yields to a smoother trajectory of the cyclists.
  • VD: Increasing VD for pedestrians provoke a greater reject effect from the cyclists (and other pedestrians).
  • Noise: In order to avoid a deadlock between two cyclists, this value should be greater than the default value (1.2).
  • Grid size: Since we do not have to deal with surfaces, but with narrow on long paths, the grid size has only a moderate influence.

Even if the bidirectional flows are much more realistic than with the strategy ‘pedestrians as little cars’, the main issue with this approach remains the very late reaction of pedestrians or bicycles to other agents. In reality, especially cyclists would react much earlier if another cyclist is approaching in opposite direction. Instead, the reaction occurs very late, only few meters before the collision (Figure 6). Hence, the cyclists should have a main force influencing the pedestrians and other bicycles much further ahead.

Figure 6: Basic problem of scenario 4: late reaction of riding in the opposite direction
Figure 6: Basic problem of scenario 4: late reaction of riding in the opposite direction

An adaptation of the 3D Model of bicycles is also needed, because the legs of the cyclists are always moving at constant (fast) speed, even if they stop, making them look like a nervous insect.

Last but not least, the bicycles cannot be simulated anymore with car traffic (e. g. bicycle lane in parts on sidewalks and road). This restriction leads to problems, especially if you plan to simulate a whole network, where a cycle lane merges in a shared space.

We have also a youtube video resuming the main aspects of this article.

Alternative approaches

Since possibilities to simulate a proper interaction between pedestrians and bicycles on a narrow path in Vissim are limited, further research work is needed. One of the most promising approach is shown by a group of researcher s around Prof. Dr. Martin Fellendorf from the Graz University of Technology in Austria. They propose to extend the Social Force model to vehicles in order to model shared including all vehicle and pedestrian types. The so called Social Force based Vehicle Model was presented at the Transport Research Board Conference in 2012. The methodology, explained in this paper, features a multiple model architecture including an Infrastructure Model (shared spaces without spatial separation), Operational Model (Social Force Model with added vehicle dynamics) and Tactical Model (conflict detection and handling using game theory).

Liang, Mao & Xu proposed in 2012 the psychological-physical force model, an adaption of the social force model which represents the cyclists as ellipses and utilizes two regimes, free flow traffic and congested traffic, to model bicycle traffic. While the model generated realistic results for traffic flow on separated cycling lane, the model has not been tested for mixed traffic conditions.

Bani Anvari and her colleagues presented in 2015 the development of new three-layered mathematical model for heterogeneous agents (vehicles and pedestrians) in a shared space environment with single surface pavements, no lane discipline and identical priority for all road users.

Another, more straightforward approach to model shared spaces was proposed by traffic engineer Stuart Gibb in 2015. He only focussed on the interactions between pedestrians and cars. The innovative idea is to replace the cars with groups of closely-packed dummy pedestrians, so that the real pedestrians recognise the vehicles and walk around them instead of simply cross them. In order to recognise precisely where the vehicle stands and replace it with dummies, a fine-meshed grid must be created. In addition, priority rules, links, detectors, pedestrian areas and pedestrian routes have to be built. Kindly, he shared his code that directly implements this approach through Vissim’s COM interface in this report.

Implementing a new behavioural model as the magic bullet?

As already mentioned, a realistic simulation of pedestrians and bicycles interactions on narrow paths is challenging to reproduce using out-of-the-box Vissim, and probably in all other traffic microsimulation tools.

Our tests were of course quite basic as they only tested how Vissim can be configured to simulate mixed traffic conditions between pedestrians and cyclists without actually changing exsting behavioural models. But it became clear that the results or not very satisfying and that there is a need for implementing a new behavioural model to simulate traffic on paths that are shared among pedestrians and cyclists, but also shared space situations in general.

Therefore, we also join the race to develop models and code for simulating such situations. This will ultimately allow us to combine it with parametric 3D models and create interactive Virtual Reality scenarios of shared urban. We are looking forward to work on this quest, stay tuned!

Bicycle infrastructure in Singapore: An overview

Why don’t we cycle in Singapore?

Cycling is one of the most space efficient and sustainable mode of transportation in cities. Indeed, these pedal powered two-wheelers are faster than our own two feet, and can be as flexible as cars, without the emissions and noise. So why does cycling remain a less preferred mode of transport in Singapore?

“Because the city is not designed for cycling”

This is a common concern of potential cyclists in Singapore. Urban development patterns and morphology can hinder or facilitate active mobility. Indeed, a growing body of research provides evidence of a link between the built environment and active living (see also Ewing et al., 2003). Empirical evidence has shown that mixed use compact cities are more attractive for pedestrians and cyclists while low density mono-functional sprawls deter active mobility (see also Saelens, Sallis and Frank, 2003). It follows that in order to reduce our dependence on automobiles and move towards a car-lite future, dense mixed use urban development designed for human scale and walking speed is essential.

Singapore has always aspired to follow a compact mixed use development model. This is evident in the various Concept Plan and Master plan statements (published by the Urban Redevelopment Authority) that propose a ‘decentralization strategy’ (Concept Plan 1991), to ‘bring jobs closer to homes’ (Concept Plan 2001), by ‘providing greater mobility with enhanced transport connectivity’ (). Even so, only 1% of the daily trips are on bicycles, compared to 22% in Amsterdam or the 14% in Tokyo.

However, typical new town developments in Singapore are influenced by mid-20th century modernist planning principles like segregated zoning and traffic networks. There is very little to no mixed use developments, large distances between crossings, homogenous housing types, which are connected by wide high speed roads. These streets and neighborhoods appear to be designed for the convenience of cars, where being a cyclist is clearly uncomfortable (or unsafe). Despite Singapore’s well-intentioned efforts to become a cycle friendly city, the existing urban development patterns and street design do little to support these efforts. This mismatch is also one of the main raison d’être of our research project Engaging Active Mobility at the Singapore ETH Centre.

“Because tropical weather is not suitable for cycling”

Singapore’s geographic location close to the equator means a humid and hot climate all year round. Popular perception is that the weather is too harsh for cycling. But according to some, this is only a matter of expectation and conditioning. Most of our team cycles to work on a regular basis, as do a number of cyclists in cities around South East Asia that share the same weather concerns.

The scientific workshop ‘Creating Healthy Places through Active Mobility’, initiated by the Centre for Liveable Cities in 2014 in Singapore, also arrived at the same conclusion. The international expert team asserted that tropical climate conditions are not a limiting factor for establishing a bike culture in Singapore. More than ever, technical and operational measures should be taken to ensure the connectivity and the continuous movement in order to reduce the physical effort. In addition, ancillary facilities like showers, lockers and changing rooms can be provided at workplaces for the convenience of cyclists.

“Because policy and public behavior does not support it”

Even though owning a car in Singapore is restrictively expensive , motorized car traffic still enjoys a privileged status on Singapore streets. Traffic lane widths are generous, the roads straight and traffic signals are programmed to maximize car throughput while pedestrians must cope with waiting times beyond 90 seconds. In addition, most motorists do not seem inclined to share their space with cyclists. Hence, there are plenty of reasons for cyclists to shy away from roads and pedal along sidewalks instead. Frequent conflicts on the sidewalk with pedestrians and cars at garage entrances restrict continuous movement of cyclists who need to repeatedly break and accelerate, which can be particularly strenuous. While cycling on the sidewalk currently still illegal but remains largely unenforced, the government has recently accepted the recommendations of the Active Mobility Advisory Panel to legalise cycling on sidewalks and expects to put the new regulation in effect towards end of 2016.

Given that the amount of people who are cycling in Singapore is very small, neither the car drivers, nor the pedestrians are really aware of the presence of the bicycles. As Figure 1 shows, pedestrians often encroach upon designated bike paths.

Of course a behavioral change is called for, and there have been many efforts lately to institute this change. For example, the OCBC Cycle Singapore festival runs a ‘1.5 meters matter’ campaign encourages drivers to give cyclists more room. More recently, URA (Urban Redevelopment Authority) launched the monthly car-free day, to sensitize the general public to active mobility. LTA (Land Transport Authority) will launch an Active Mobility Campaign to increase awareness of the new policies, rules and codes of conduct.

Pedestrian standing on bicycle path (left) and bicycle bridge used as shortcut by pedestrians (right)
Figure 1: Pedestrian standing on bicycle path (left) and bicycle bridge used as shortcut by pedestrians (right)

Existing bicycle infrastructure in Singapore

To surmount the shortcomings related to cycling on the road and on sidewalks, Singapore’s planners have so far put forward two types of design bicycle infrastructure: Park Connector Networks and Intra-town cycling. The Park Connector Network connects parks through a continuous network, exclusively for cyclists and pedestrians. The usage of this network is mainly limited to leisure activities. The paths follow a scenic, sometimes circuitous route instead of the most direct one, guiding people along rivers, parks and residential areas. Shelters are provided frequently for weather protection (see Figure 2).

Figure 2: Weather protection along the Park Connector from Pasir Ris to Tampines by means of a shelter (left) or protected by the MRT bridge (right)
Figure 2: Weather protection along the Park Connector from Pasir Ris to Tampines by means of a shelter (left) or protected by the MRT bridge (right)

Even though cycling on Park Connectors is a pleasant way to discover Singapore, it is not suited to commuters needs. On a sunny day, the detours and recurring interruptions as illustrated in Figure 3, can take a toll on the rider.

Figure 3: Park Connectors are still interrupted frequently, by a road crossing (left) or when the infrastructure ends abruptly (right)
Figure 3: Park Connectors are still interrupted frequently, by a road crossing (left) or when the infrastructure ends abruptly (right)

A second type of cycle network – intra-town cycling in specific neighborhoods with social housing (HDB) – focus on short distance trips to reach popular destinations such as subway stations, schools or shopping centers. The Land Transport Authority of Singapore selected 7 HDB Towns to improve cycling infrastructure by the end of 2015. Recently, a bicycle network was constructed in Pasir Ris, mostly consisting of bike lanes that are well segregated with directional signage and contrasting paving material (see Figure 4).

Figure 4: Design of bicycle lanes in Pasir Ris: a bus stop bypass (left) and a feeder lane for the MRT station (right)
Figure 4: Design of bicycle lanes in Pasir Ris: a bus stop bypass (left) and a feeder lane for the MRT station (right)

However, by putting the bike lanes between the footpath and adjacent buildings, any building entrance becomes an area of conflicts with pedestrians and hence only allows cycling at low speeds (Figure 5).

Figure 5: Bicycle lanes are interrupted frequently by pedestrian crossings
Figure 5: Bicycle lanes are interrupted frequently by pedestrian crossings

In addition, bicycles do not have priority at any time, as prescribed in the cycling rules, causing many speed interruptions (Figure 6). At some places, a sign asks to the cyclists to dismount and push the bike through the conflict area.

Figure 6: Speed profile of a bicycle test ride in Pasir Ris
Figure 6: Speed profile of a bicycle test ride in Pasir Ris

Bicycle lanes sometimes end abruptly near obstacles like overhead bridges and lack of priority on intersections impedes a continuous cycling flow (Figure 7). Reduced speeds and added discomfort makes cycling a less than ideal transportation option on these networks.

Figure 7: Other types of interruptions like narrowing bicycle lanes (left) and crossings of access roads or parking exits (right)
Figure 7: Other types of interruptions like narrowing bicycle lanes (left) and crossings of access roads or parking exits (right)

Another popular way to expand the bicycle network in Singapore is  widening sidewalks, for example the new infrastructure in Tampines (Figure 8). However, this design with mixed zones also evokes frequent interactions between pedestrians and cyclists. But interestingly, according to our observations cyclists and pedestrians self-sort to a certain degree as cyclists chose often to pedal towards the side of the road.

Figure 8: 'Widening' of sidewalks in order to create mixed pedestrians and cycling zones (left) and abrupt end of bicycle lane (right), both examples from Tampines
Figure 8: ‘Widening’ of sidewalks in order to create mixed pedestrians and cycling zones (left) and abrupt end of bicycle lane (right), both examples from Tampines

An important support infrastructure for cycling is parking facilities at destination. Due to the efforts of the city authorities and the transport companies, the capacity of parking amenities close to train station has augmented considerably over the last years. Nowadays, bicycle parking, sometimes covered as shown in Figure 9, are available on most train stations. However, given that flat tires are frequently observed, a fair share of the parked bicycle is probably not very frequently used, but calls for better parking management solutions.

Figure 9: Efforts have been made over the last years to expand bicycle parking facilities at MRT stations (here: Pasir Ris MRT)
Figure 9: Efforts have been made over the last years to expand bicycle parking facilities at MRT stations (here: Pasir Ris MRT)

The next generation of cycling infrastructure

Even though public transport remains the main focus in the Land Transport Master Plan 2013 the Land Transport Authority of Singapore plans to expand the network of sheltered walkways and dedicated bicycle paths considerably by 2020. The efforts are directed at completing the Park Connector Network reaching a network length of over 700 km by 2030. The announcement of works to transform Ang Mo Kio in a new model walking and cycling town in December 2015 earmarks the start of a new generation of intra-town cycling infrastructure and serves as a pilot for similar schemes for all public housing (HDB) neighbourhoods. The renderings show that the cycling paths adjacent to the sidewalk closer to the road, which will reduce the number of potential conflicts with pedestrians. Near bus stops, ‘Pedestrian Priority Zones’ are proposed where cyclists are expected to slow down or dismount and push. On road cycle lanes are only planned along minor roads which means that cyclists are expected to share pedestrian crossings to turn or change lanes. While they are allowed to cycle across mid-block crossings, they are required to dismount and push at junctions which hinders continuous movement. However, a new segregated cycling highway cutting along (and below) an existing MRT corridor will allow continuous and sheltered cycling to the MRT station.

Figure 10: Artist's impression of some of the new design to be implemented in Ang Mo Kio. (Images: URA)
Figure 10: Artist’s impression of some of the new design to be implemented in Ang Mo Kio. (Images: URA)

For longer-distance connectivity, the planned North-South Expressway will be reconfigured to be part of a “North-South Corridor” that will include express bus lanes and a cycling trunk route to the city (Figure 7). Again, the cycling lane is planned adjacent to the sidewalk towards the road which generally lowers the number of conflicts with pedestrians but requires well thought out design solutions around bus stops.

Figure 11: Artist's impression of the North-South Corridor. (Image: Land Transport Authority)
Figure 11: Artist’s impression of the North-South Corridor. (Image: Land Transport Authority)

The research puzzles that lie ahead

While the growing interest in cycling and walking, and the commitment of planning authorities to providing support infrastructures in Singapore are promising, there are several open questions that remain unanswered, opening interesting avenues for research.

Cycling city role models such as Copenhagen, Amsterdam or more recently also New York and London are very different from Singapore with regard to the urban fabric and climatic conditions. The hierarchical street network restricts connections between neighbourhoods along low-traffic roads. At the same time, the main arterials are usually characterised by several rather wide lanes inviting motorists to speed well above the allowed 60km/h. Frequent bus services and stops cause additional challenges to fit in appropriate cycling infrastructure.

Park Connectors seem ideal to overcome those challenges. Once the network gaps are closed, the segregated bicycle paths ensure stress-free and continuous movement through pleasant environments. Unfortunately, the origin or destination of a utilitarian (not leisure) cycling trips seldom is located directly along a Park Connector but usually within a town.

This raises the question how existing roads can and should be redesigned to address cyclists’ concerns of perception of safety and continuous movement. In particular, previous and ongoing initiatives to improve the cycling infrastructure clearly struggle with the question whether and when cyclists should belong to pedestrian or vehicular realm. Based on the experience in other cities, it is clear that the propensity to cycle is directly related to how well the infrastructure caters to a cyclist need for safe, direct and comfortable routes. When designing such infrastructure, planners face recurring dilemmas and trade-offs. A cycling route along a major road might serve directness, but designing it to be safe and support continuous movement can be very challenging as potential conflicts with buses, cars and pedestrians must be considered. To meaningfully do so, we must understand how people perceive and react to different design options.

Similarly, when designing a network of cycling routes, key planning questions are also where to invest and how much. Cycling demand not only directly depends on the quality of the infrastructure, but potential flows are also contingent on land use and the distribution of origins and destinations. In addition, how far people are willing to cycling is dependent on the required physical effort which again is dependent on the type of cycling infrastructure, climatic conditions and bicycle technology. Integrating all those factors in a consistent planning framework makes cycling network design can be a daunting challenge.

Our research project, launched in February 2016, aims at answering these questions and challenges. By employing Virtual Reality applications in surveys we attempt to distill behavioral evidence that can guide planners through these dilemmas when designing future cycling infrastructure. By providing new spatial analysis tools that integrate with existing transport demand models and also harness big data sources such as public transport smart card records, we aim at solving the puzzle of cycling network design and find out how cycling can become a viable commute option in Singapore. As we embark on this journey, we welcome very much your comments and suggestions to support an engaged discussion!

New ways to count pedestrians

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.

Figure 3: Comparison of results from IP camera and manual counts

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
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 😉 !


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.