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Tactical urbanism is an innovative urban design approach consisting of interim street transformations enabling public authorities and administrations to temporarily requalify deteriorated or misused urban spots to be regenerated and reinstated into their original purposes. Such an approach is characterized by limited financial resource usage and streamlined bureaucratic procedures and requires the involvement of the public, especially locals or usual users of the area, who can have their say during the project design and the later assessment phases.
- tactical urbanism
- OD matrix estimation
- Floating Car Data
1. Tactical Urbanism
The tactical urbanism approach has been increasingly promoted by public administrations to boost the implementation of rapid and effective changes in the urban setting under their competence. Such street experiments are aimed at establishing the idea that streets should belong to people rather than traffic [1] by physically reducing road space capacity for vehicles in favour of pedestrians and soft mobility. This practice allegedly leads to traffic evaporation, referring to the reduction in traffic flows as a consequence of capacity-limiting interventions on the transport network [2]; such phenomenon can be intended as the opposite of induced traffic, which results from an expansion of road capacity (e.g., opening of a new road, widening of the road via new lanes). While the literature has focused a great deal on induced traffic implications on mobility pattern variations, so far very few researchers have investigated traffic evaporation potentially caused by reallocation scheme interventions, despite the current and popular need to switch to more sustainable modal choices than the private car. There are studies on “disappearing” traffic caused by road closures, particularly on the physiological bottlenecks of road networks, e.g., bridges [3] and tunnels [4], either planned or not. In both studies, a wide variety of traffic data could be analysed to draw conclusions as, besides traffic counts in strategic spots of the network, interviews with travellers could also give information about behavioural changes in trip generation and distribution, as well as route choice. Overall, common results from these studies suggest that a decrease in traffic flows should be expected when implementing road space reallocation schemes or, more generally, interventions that reduce street capacity, with traffic behaviour change in the new scheme area to be proportional to the level of disruption to the network. Furthermore, even though the capacity of the network is downgraded, road congestion tends to be less severe than conventional traffic models would suggest. However, because of the poor research effort involved as of today, correlation among studies remains difficult due to different geographical contexts and types of interventions.
Research also lacks work exploring impacts on a wider perspective on the network, so not only in the local intervention area but also from a meso-scaled point of view. A valuable exception is [5], which exploited datasets of traffic counts provided by permanent sensors to evaluate the extent of traffic evaporation following the extensive implementation program of multiple tactical urbanism interventions in the Eixample district of the city of Barcelona, Spain. The study investigated not only streets directly affected by interventions but also assessed traffic levels in a buffer area within 500 m, leading to invalidation of the assertion that traffic would simply gather onto more convenient paths and congest roads in a limited buffer area, as results suggest that overall traffic diminished, with a very low relative increase in the streets adjacent to interventions.
2. Floating Car Data
Besides traditional methods, lately more and more traffic data sources and collection techniques have been either improved or first implemented, each of them featuring their own advantages and disadvantages according to the usage and the collection purpose. The aim of this research effort is to progressively outdate traditional techniques such as travel surveys (household, on-board, etc.), which can be time- and resource-consuming and also poorly accurate for low-sampled trips.
As comprehensively portrayed by [6], road traffic data collection methods can be distinguished based on whether measurements are performed by sensors—or people, in the case of manual counts—located along the roadside or by vehicles themselves acting as moving sensors for the road network. This second cluster is known as Floating Car Data and works by collecting real-time traffic data through consecutive positions of equipped vehicles via mobile phones or GPS devices, along with complementary data such as instantaneous speed and direction of travel. By providing high-quality and cost-effective data, as there is no need of implementation of hardware in situ but only on vehicles, Floating Car Data (FCD) is a promising alternative to existing technologies for road data collection. It is also crucial in the development and functioning of Intelligent Transport Systems (ITS), which mostly rely on precise real-time information on traffic conditions through the network. It must be remembered though, that such raw data do not explicitly provide information to calibrate or validate estimates on behavioural choice, which travel surveys can usually offer, even though an abundance of available traffic data can usually compensate such missing information.
Based on the connectivity option, FCD can be GPS-based or reliant on cellular phone networks; while GPS provides a 10 times better precision but suffers lack of vehicles equipped with suitable devices, cellular-based technology compensates low accuracy with a large sample size, corresponding to a wide coverage.
Lately, Floating Car Data has been used as ground data for research studies addressing mobility tasks and issues, such as detecting and analysing urban patterns from GPS traces [7], estimating traffic delays and network speeds from low-frequency GPS taxi trajectories [8], detecting traffic congestion and incidents [9] or estimating or updating OD matrices.
3. OD Matrices Estimation
Indeed, Origin–Destination matrix estimation can be a challenging requirement prior to transport network simulations. In the framework of four-stage traffic models, it can be thought of as the inverse procedure of the traffic assignment step [10]; while the latter loads the network with flows determined according to estimated or observed traffic demand and route choice models, OD matrix estimation goes the other way round, focusing on estimating path flows (which once aggregated represent OD pair flows) based on available records of link flows in the reference period. Such data is usually available through travel surveys or traffic counts in specific sections of the transport network under examination, either manually or via automated sensors (magnetic spires, cameras, etc.).
Traditional methods of vehicular demand estimation through traffic count data recorded on links of the network require ground truth OD data: such a process is usually an update of an outdated Origin–Destination matrix via traffic counts collected in the reference period, with data in the “old” matrix being attributed a level of confidence in relation to their age.
Estimation of OD matrices from scratch is a challenge from a mathematical standpoint, as errors affecting route choice models and observed flow data can lead to undetermined problems (no existing matrix capable of representing actual observed flows). Moreover, despite the wider coverage with respect to interviews or surveys, traffic count campaigns, either manual or via fixed automated sensors, usually provide flow information only on a limited number of links of the network, resulting in underdetermined equation systems whose solution need to be estimated through regression procedures such as constrained generalized least squares or maximum likelihood algorithms. Furthermore, the absence of an outdated OD matrix as ground data for the estimation process strongly affects Origin–Destination pairs that are not covered by any of the planned traffic counts.
Due to the current availability of large streams of passively collected data, data-driven methods have started to be developed and validated by academic research as an efficient alternative to traditional OD estimation methodologies. For instance, in Bonnel et al. [11] and later in Fekih et al. [12], data from mobile phone networks were used to estimate OD matrices in the Rhône Alpes region in France. Croce et al. [13] integrated FCD data to traditional models and surveys at sub-regional level to enhance predictions and analyses of transport planners. Demissie et al. [14] analysed GPS trajectory data over one year to estimate Origin–Destination flows of trucking vehicles within the province of Alberta, Canada. Ge et al. [15] used aggregated data of GPS traces to avoid privacy issues and implemented a sequential updater based on maximum entropy principle to update an outdated matrix. Tolouei et al. [16] validated such methods by comparing matrices obtained through roadside interviews together with trip-end and gravity models and through the application of mobile phone data. The study states that trip matrices developed through mobile data were as accurate as the ones estimated through conventional models if refined and adjusted to remove intrinsic biases and limitations. Furthermore, the advantage of larger sample sizes allowed mobile data to estimate in a more consistent manner trips where no roadside observed data were available. Krishnakumari et al. [17] proposed a method applicable in the presence of 3D supply patterns only (sample size and speed values per each time period) on all segments of the network, consisting of a large equation system integrated with principal component analysis in the case of severely underdetermined systems (typical of larger networks). The research experiment featured fundamental assumptions regarding route choice, e.g., cutting off the number of considered paths and assigning a proportionality coefficient to each path in the OD pair-specific set calculated through a route choice model.
Overall, according to the nature and the aggregation level of available datasets, the literature provides appropriate methods and solutions depending on the purpose with which data can be elaborated.
To sum up, there is a need to understand how tactical urbanism interventions, which usually reduce road capacity as roadwork closures, sized as the one under examination, affect traffic patterns at the neighbourhood level; in order to do so, FCD can be a promising tool, with recent examples in the literature analysing traffic exclusively with big data and data-driven methods. Furthermore, in the literature, an OD matrix extraction method has been found, suitable for the available dataset.
This entry is adapted from the peer-reviewed paper 10.3390/su151712716
References
- Bertolini, L. From “streets for traffic” to “streets for people”: Can street experiments transform urban mobility? Transp. Rev. 2020, 40, 734–753.
- Cairns, S.; Atkins, S.; Goodwin, P.; Bayliss, D. Disappearing traffic? The story so far. Proc. Inst. Civ. Eng. Munic. Eng. 2002, 151, 13–22.
- Hunt, J.D.; Brownlee, A.T.; Stefan, K.J. Responses to Centre Street Bridge Closure: Where the “Disappearing” Travelers Went. Transp. Res. Rec. 2002, 1807, 51–58.
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- D’Andrea, E.; Marcelloni, F. Detection of traffic congestion and incidents from GPS trace analysis. Expert Syst. Appl. 2017, 73, 43–56.
- Cascetta, E. Teoria e Metodi Dell’ingegneria dei Sistemi di Trasporto, 2nd ed.; UTET: Torino, Italy, 1998.
- Bonnel, P.; Fekih, M.; Smoreda, Z. Origin-Destination estimation using mobile network probe data. Transp. Res. Procedia 2018, 32, 69–81.
- Fekih, M.; Bellemans, T.; Smoreda, Z.; Bonnel, P.; Furno, A.; Galland, S. A data-driven approach for origin–destination matrix construction from cellular network signalling data: A case study of Lyon region (France). Transportation 2021, 48, 1671–1702.
- Croce, A.I.; Musolino, G.; Rindone, C.; Vitetta, A. Estimation of Travel Demand Models with Limited Information: Floating Car Data for Parameters’ Calibration. Sustainability 2021, 13, 8838.
- Demissie, M.G.; Kattan, L. Estimation of truck origin-destination flows using GPS data. Transp. Res. Part E Logist. Transp. Rev. 2022, 159, 102621.
- Ge, Q.; Fukuda, D. Updating origin–destination matrices with aggregated data of GPS traces. Transp. Res. Part C Emerg. Technol. 2016, 69, 291–312.
- Tolouei, R.; Psarras, S.; Prince, R. Origin-destination trip matrix development: Conventional methods versus mobile phone data. Transp. Res. Procedia 2017, 26, 39–52.
- Krishnakumari, P.; van Lint, H.; Djukic, T.; Cats, O. A data driven method for OD matrix estimation. Transp. Res. Procedia 2019, 38, 139–159.
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