Optimization of Daily Activity Chains
The initial assumption was that some activities performed by the users during a day are not necessarily fixed temporally and spatially, therefore they can be carried out in different times or locations. The order of flexible demand points can be also changed. Before and after an activity a travel phase is realized by different transportation modes. By introducing flexible demand points, it is possible to find all combinations and to choose the optimal activity chain by implementing a solution for the TSP-TW problem.
The developed algorithm takes into consideration many constraints, as opening times of the shops or maximum waiting times before the planned arrival. During the implementation 3 different modes of transportation were determined: car, public transport and public transport with car-sharing opportunity. The optimization criterion was the minimum travel time, as the most important parameter. Also other parameters can be taken into account (e.g. comfort features), but these are generally hard to be quantified.
As an output of the optimization Pareto optimal results are presented, where the parameters are the number of postponed activities and the total travel times. The simulation of activity chain optimizations was performed on arbitrarily chosen test networks in Budapest using Matlab. In case of car usage about 8%, with public transport about 10% and with car-sharing opportunity about 14% decrease of the total travel time was realized. The elaborated method can be build in an advanced information service.
Related publication:
Domokos Esztergár-Kiss: Optimization of multimodal travel chains
PhD thesis, BME Dept. of Control for Transportation and Vehicle Systems, 2016.
Concept of Smart Signal Heads
Demonstration video:
We are doing a project in our laboratory which called “Smart Signal Head” in road traffic signal control. The base of the idea is that we place a PLC (but it could be whatever computer unit) into the signal head which can switch the signal lamps bulbs and keep in touch with the remote control for further leading functions. The biggest advantage of the concept is that the huge quantity of the long, multi-wired cables can be replaced with one single UTP cable. Since the logical module is situated within the signal head, the cable connection between the signal head and the module means centimeters. This system results significant cost savings.
Universal traffic control
Traffic control with Smart Signal Heads
Another benefit of the smart signal head system is the simple applicability for special cases like traffic signalization during road construction. Moreover, the installation is easier and faster. We set up our smart system with Siemens LOGO! 12/24 RCE PLCs, Actros VTC 3000 traffic control unit and Siemens/Elba LED signal heads. The communication works through TCP/IP protocol. There are UTP cables between the modules.
The safety critical functions of the system:
- recognition of prohibited (conflicting) signals - in that case the flashing yellow mode is activated,
- in case of inside errors it switches to fail-safe position state (depending on the error it will be flashing yellow light or dark mode,
- checking the right green light combination, i.e. prohibited greens cannot be on at the same time (check the existence of the intermediate times),
- checking the brake of the signal lamps
As the PLC program contains itself the flashing yellow function, false signals cannot occur (in case of error the system switch immediately to the flashing yellow light mode).
The specialty of this technology is represented by the method of the safety critical functions. The continuous checking of the signal heads must be ensured by data transmission.
Future improvement:
- replace the PLCs with microcontoller
- change the TCP/IP protocol to UDP or serial communication
- increase the intelligence of the signal heads to a higher level where they can work without any main traffic control unit.
Cellular Network Events for Traffic State Estimation
Several measurement technologies exist. Nevertheless, it is a general aim to create an efficient measurement system in high traffic areas of the cities. Ideally, each road link of the network could provide traffic information, e.g. by loop detectors, camera, on-board units. A novel approach to this problem is the traffic measurement and estimation through the events generated in the cellular phone network. Thus, one of our main research subjects is the applicability of mobile signaling events for traffic measurement. Related to this research our laboratory has been in cooperation with Nokia Siemens Networks to develop travel time estimator since 2011.
The use of signaling events of the cellular network opens a way for road traffic estimation and modeling in a macro level. This can be realized as real-time algorithm providing a base for adaptive intelligent traffic management. The basic elements of cellular phone network (Fig. 1) are the cell (covered by the base station) and the Location Area (LA) combining several cells. The shape of them depends on several factors and is never circles exactly. Nevertheless, just for the better explanation: the radius of the cells is about a few hundred meters and the same of the LA is about a few kilometers in urban area.
Figure 1: Schematic representation of the GSM network (Küpper, 2005)
Two characteristic events of the network are Handover (HO) and Location Area Update (LAU) which are generated by the transitions between the cells and LAs (Fig. 1). HO occurs when the mobile phone is in call and changes the cell. LAU is generated by idle phones (not in calls) changing the LA. HO/LAU events are automatically stored by the operator of the telecommunication network through the base stations. Our research aim is to develop efficient algorithms which exploit the knowledge of HO/LAU events for traffic estimation.
The mobile users create traces in the traffic network through their HO/LAU events depicted in Fig. 1. These data can be efficiently utilized in an aggregate way to estimate traffic even in real-time mode. The proposed method has two steps. First, a trip matrix of the traffic network must be created. Trip or OD (origin-destination) matrix determines the traffic flows between each OD pairs of the network (Fig. 2).
Figure 2: Structure of the OD-matrix (Luspay et al., 2011)
OD matrix can be created through traditional methods (surveys) or by the appropriate use of the LAU events (Calabrese et al., 2011). The second step of the method consists of the traffic assignment which is generally applied based on OD matrix. Typically, traffic assignment is an optimization procedure which determines the route choices with traffic volumes between the OD pairs. The determination of the potential paths through traffic assignment is an easy task. However, the traffic volumes assigned to the paths may easily become inaccurate since assignment considers generally “only” the data of the OD matrix and the network topology. A potential solution to the reliability problem is given by the use of HO events. Although HOs are generated only by phones in call, by collecting all of them the typical traffic behaviors can be identified. Practically, the trajectories revealed through HOs can be fitted to the paths defined by the traffic assignment. Therefore, more accurate estimates are provided concerning the traffic volumes.
The results of the research has already been published (Tettamanti et al., 2012 and Tettamanti et al., 2014). The paper presents the preparation step for the path-fitting method described above. For the modeling of the radio cells the Voronoi tessellation was applied (Candia et al., 2008) which requires only the coordinates of the base stations (Fig. 3).
Figure 3: Voronoi tessellation for the modeling of radio cells
The method is represented through the results of a test measurement in Budapest. The cells provided by the HOs of the test device were fitted to the routes resulted from the traffic assignment. One path was found as the most likely one among the potential routes. Path 4 (Fig. 4) had the smallest squared deviation compared to the others.
Figure 4: Path with the smallest squared deviation between the origin-destination pair
We plan to investigate further possibilities in the field of mobile phone event based traffic applications, as well the efficient fusion of these data with other measurements (e.g. loop detector).
Related publications
Luspay, T., Tettamanti, T., Varga I.: Forgalomirányítás, Közúti járműforgalom modellezése és irányítása. Typotex Elektronikus Kiadó Kft. ISBN 978-963-279-665-9, 2011
Tettamanti, T., Demeter, H., Varga, I.: Route choice estimation based on cellular signaling data, Acta Polytechnica Hungarica, 9(4):207–220, 2012
Tettamanti, T., Ludvig, Á., Varga, I.: Travel time estimation in urban road traffic networks based on radio signaling data, MITIP, Budapest, 2012, pp. 514-527. ISBN 978-963-311-373-8
Tettamanti, T., Varga, I.: Urban road traffic estimation based on cellular signaling data, MITIP, Budapest, 2012, pp. 220-230. ISBN 978-963-311-373-8
Tettamanti T and Varga I (2014), "Mobile Phone Location Area Based Traffic Flow Estimation in Urban Road Traffic", Columbia International Publishing Advances in Civil and Environmental Engineering. Vol. 1(1), pp. 1-15.
References
Calabrese, F., Di Lorenzo, G., Liang, L., Ratti, C.: Estimating Origin-Destination flows using mobile phone location data. 2011, Pervasive Computing IEEE, 10(4):36-44.
Candia, J., González, M. C., Wang, P., Schoenharl, T., Madey, G., Barabási, A.-L.: Uncovering individual and collective human dynamics from mobile phone records. Journal of Physics A: Mathematical and Theoretical, 41(22):224015, 2008.
Küpper, A.: Location-based Services. John Wiley & Sons, 2005.
