**4.2 Urban sensing**

Urban sensing is a paradigm on collecting information about systems and the environment, which are closely related to and affected by human activities. Most prior work on sensor networks is based on collecting and processing environmental data using a static topology and an application-aware infrastructure. Urban sensing, on the other hand, involves collecting, storing, processing and fusing large amounts of data related to everyday environmental changes resulting from human activities, vehicles and other agents. This form of sensing is performed in highly dynamic and mobile environments.

Urban sensing applications are emerging in several areas. A good example of human centric urban sensing is Active Mapping. It is built on top of a geographical map, and collects and exchange information about human activities such as location and other details. Therefore it provides a platform for people interaction and also serves as an interface for registering context-aware events. An important application area within urban sensing is urban information systems. A common design approach is to build a publish-and-subscribe mechanism and provide differentiated services to meet individual user's interests. Therefore, real-time, context-aware and online information management systems of urban sensing applications are highly encouraged.

Urban sensing can be primarily divided into two kinds: static infrastructure and humancentric urban sensing.

The former includes urban multifunction traffic lights control system, equipped with sensing infrastructure that has often been an effective measure applied to regulate vehicle ow inside cities. This static infrastructure uses real-time measurements such as inductive loops or pattern-recognition digital cameras to decide the suitable trafc signal. Infrared remote control apparatus recognizes the signal light control of each intersection. Moreover, these infrastructures are equipped with communication networks that enable adaptive coordination between different intersections in order to improve the trafc ow globally.

The latter has typically been used in the context of human-in-the-loop sampling scenarios where human involvement is mainly in the sampling or the sensing process (through handheld mobile devices etc.). In (Lim et al., 2009) authors propose to redefine or extend the definition of human-centric urban sensing. In the proposed framework, human-centric urban sensing refers to human involvement in the data assimilation, processing, inference as well as decision, control and feedback processes.



Urban sensing is a paradigm on collecting information about systems and the environment, which are closely related to and affected by human activities. Most prior work on sensor networks is based on collecting and processing environmental data using a static topology and an application-aware infrastructure. Urban sensing, on the other hand, involves collecting, storing, processing and fusing large amounts of data related to everyday environmental changes resulting from human activities, vehicles and other agents. This

Urban sensing applications are emerging in several areas. A good example of human centric urban sensing is Active Mapping. It is built on top of a geographical map, and collects and exchange information about human activities such as location and other details. Therefore it provides a platform for people interaction and also serves as an interface for registering context-aware events. An important application area within urban sensing is urban information systems. A common design approach is to build a publish-and-subscribe mechanism and provide differentiated services to meet individual user's interests. Therefore, real-time, context-aware and online information management systems of urban

Urban sensing can be primarily divided into two kinds: static infrastructure and human-

The former includes urban multifunction traffic lights control system, equipped with sensing infrastructure that has often been an effective measure applied to regulate vehicle ow inside cities. This static infrastructure uses real-time measurements such as inductive loops or pattern-recognition digital cameras to decide the suitable trafc signal. Infrared remote control apparatus recognizes the signal light control of each intersection. Moreover, these infrastructures are equipped with communication networks that enable adaptive coordination between different intersections in order to improve the trafc ow globally.

The latter has typically been used in the context of human-in-the-loop sampling scenarios where human involvement is mainly in the sampling or the sensing process (through handheld mobile devices etc.). In (Lim et al., 2009) authors propose to redefine or extend the definition of human-centric urban sensing. In the proposed framework, human-centric urban sensing refers to human involvement in the data assimilation, processing, inference as

into the decision index, which specifies the ride quality (T. Lee et al., 2009).

form of sensing is performed in highly dynamic and mobile environments.

sensing applications are highly encouraged.

well as decision, control and feedback processes.

far-end monitoring center (U. Lee & Gerla, 2010).

**4.2 Urban sensing** 

centric urban sensing.

positioning performance and availability (Chen et al., 2009).

speech suggestion of CW will be activated and the evaluation degree is also sent to the

According to research of Lee and Gerla (U. Lee & Gerla, 2010), some technologies for communications in vehicular environments are DSRC/WAVE, cellular networks, WiMAX/802.16e, WiFi/802.11p. These technologies will enable operations related to the improvement of trafc ow, highway safety, and other ITS applications in a variety of application environments.

Given the above sensors and communications technologies, it is possible summarizes vehicular networking scenarios as shown in figure 2.

Fig. 2. Wireless vehicular networking scenarios.

Vehicles only equipped with DSRC can operate on infrastructure-free mode (V2V only), infrastructure mode (V2I), and mixed mode (V2V and V2I) as shown in Fig. 2a. Vehicles equipped with other broadband wireless access (i.e., cellular, WiMAX), can operate on scenarios where vehicles can talk to each other via Internet as in Fig. 2b. For instance, people with smartphones and Internet access can conform a P2P overlay network via the Internet. Finally, when vehicles have both DSRC and other broadband wireless access methods, we can have a mixed access scenario (Fig. 2c). Researchers have mostly focused on the rst scenario, yet the second scenario has recently received a lot of attention due to the widespread usage of smartphones and WiBro (Lee & Gerla, 2010).

In (Hounsell et al., 2009) authors describe other models and technologies that can be used for traffic data collection. For example, inductive loops embedded are used to detect the movement of vehicles over a road surface and is extensively used in traffic responsive traffic signal systems to provide relevant information about traffic conditions such as traffic density, flows and speeds, among others, that can be used to optimize traffic flows. Beaconbased technology detects a vehicle by a 'beacon' positioned at a known location employing various technologies such as microwave, infra-red and dedicated short-range

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communication (DSRC) beacons. Closed-circuit television (CCTV) provides a mechanism to monitor traffic operations at key locations in urban networks, such as major junctions, road bottlenecks, tunnels and so on. Information of this kind of systems is used as a basis for managing traffic control strategies, for confirmation of incidents, and to record conditions or events over a period of time.

### **5. Conclusions**

One of the major priorities for governments is to define mechanisms and schemes that could help solve traffic problems that modern society faces. Governments are addressing their efforts in the use of emerging technologies as base elements for transportation system. In the last few years a suite of systems and applications for vehicular communications has emerged. This suite includes applications that can be utilized for improving vehicular safety, enhancing traffic control, and making more efficient the driver tasks and comfortable the time passengers expend inside the vehicle. With technologies like these, it is possible to develop transport systems that are capable of optimizing fuel consumption, minimizing traffic congestion, reducing CO2 emissions and more importantly reducing human casualties.

In addition, there exist an important number of private and public initiatives that have been created and are dedicated to the development and research of vehicular systems. Still, because of the characteristics of VANETs in terms of, for example, its dynamic network topology, mobility patterns, low latency, among others, development and deployment of vehicular applications is still very challenging. What is more, to correctly operate, most VANET applications require support of special infrastructure (i.e. RSU) to extend vehicles short range communication coverage enabling and extending data dissemination. Unfortunately, the number of available RSUs and OBUs in today's scenarios is still very limited and this condition limits and makes difficult to deploy and evaluate existing applications. In this chapter we have analyzed some of the main challenges that the development of vehicular networks face. We presented a general study about some of the emerging technologies that can be used for vehicular networks. We have also showed some platforms that can be used as data collectors about traffic conditions, warning or emergency situations. Successful development of VANETs and the related applications are conditioned to the definition of standards that facilitate the integration of heterogeneous systems. Similarly, the creation of strategies for increasing users acceptability and accessibility to vehicular applications and technologies is necessary. Finally, to guaranty privacy and security of users, data and applications novel mechanisms need still to be developed.

#### **6. References**


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One of the major priorities for governments is to define mechanisms and schemes that could help solve traffic problems that modern society faces. Governments are addressing their efforts in the use of emerging technologies as base elements for transportation system. In the last few years a suite of systems and applications for vehicular communications has emerged. This suite includes applications that can be utilized for improving vehicular safety, enhancing traffic control, and making more efficient the driver tasks and comfortable the time passengers expend inside the vehicle. With technologies like these, it is possible to develop transport systems that are capable of optimizing fuel consumption, minimizing traffic congestion, reducing CO2 emissions and more importantly reducing human

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**1. Introduction**

account.

**0**

**5**

*Ireland*

**A CA-Based Model for City Traffic**

Questions concerning the possible mechanisms and ultimate utility of a modal shift towards alternatives to motorised transport in the developed world represent an important topic within the context of the very current problem of 'greening' urban environments, in addition to their relevance with respect to health and social aspects of transportation. The study of physical properties of traffic through measurements and modelling is essential to forming a comprehensive view of a transportation system's characteristics for any purpose, from day-to-day traffic management to long-term urban planning, and can play an important role in the planned inclusion of non-motorised traffic in cities. The focus of our work is on cycling in Dublin which is, like other old cities in Europe, characterised by relatively dense networks of narrow streets and little flexibility with regard to infrastructure. This chapter presents a simulation model suitable for the study of heterogeneous traffic on urban networks and its application to conditions similar to those present in Dublin, where bicycle traffic is sparse, road sharing with positional discipline is the most common form of bicycle-traffic inclusion

The incorporation of multi-modality into traffic flow models, whether microscopic or macroscopic, theoretical or simulation-based, is a task of increased complexity in comparison to the mode-homogeneous case. This stems from differences in spatial occupation, speed, driver/rider behaviour and other transport mode properties. The concrete questions that arise are those of spatial representation, behaviour and inter-mode interaction modelling and all of these feature strongly where heterogeneity is due to the presence of bicycles. The predominantly urban setting of motorised/non-motorised traffic mixes adds to the modelling challenge, as intersection-based manoeuvres and interactions also need to be taken into

Bicycle and bicycle-motorised mixed traffic have a number of aspects that have been of interest to researchers. The characteristics of bicycle-only flow and related road capacities have been studied since the 1970s. This work, aimed at determining the bicycle flow fundamental diagram and defining levels of service, is reviewed in [3]. The authors present a cellular automaton bicycle-only flow model of their own, verified by means of data collected by the authors. Another cellular automaton model of bicycle-only flows, with multiple occupancy of cells and slow/fast distinction between cyclists is presented in [5]. Forms of heterogeneous flow that includes bicycles differ widely, depending on infrastructure and on locality-specific rules and customary behaviour. Corresponding models are, consequently, very diverse. The

and dedicated infrastructure and bicycle-related controls are absent.

**Including Bicycles**

*Dublin City University*

Jelena Vasi´c and Heather J. Ruskin

