The Kalker book of tables for non-Hertzian contact of wheel and rail

A new regularisation of non-elliptical contact patches has been introduced, which enables building the look-up table called by us the Kalker book of tables for non-Hertzian contact (KBTNH), which is a fast creep force generator that can be used by multibody dynamics system simulation programs. The non-elliptical contact patch is regularised by a simple double-elliptical contact region (SDEC). The SDEC region is especially suitable for regularisation of contact patches obtained with approximate non-Hertzian methods for solving the normal contact problem of wheel and rail. The new regularisation is suitable for wheels and rails with any profiles, including worn profiles.

The paper describes the new procedure of regularisation of the non-elliptical contact patch, the structure of the Kalker book of tables, and parameterisation of the independent variables of the tables and creep forces.

A moderate volume Kalker book of tables for SDEC region suitable for simulation of modern running gears has been computed in co-simulation of Matlab and program CONTACT.

To access the creep forces of the Kalker book of tables, the linear interpolation has been applied.

The creep forces obtained from KBTNH have been compared to those obtained by program CONTACT and FASTSIM algorithm. FASTSIM has been applied on both the contact ellipse and the SDEC contact patch. The comparison shows that KBTNH is in good agreement with CONTACT for a wide range of creepage condition and shapes of the contact patch, whereas the use of FASTSIM on the elliptical patch and SDEC may lead to significant deviations from the reference CONTACT solutions.

The computational cost of calling creep forces from KBTNH has been estimated by comparing CPU time of FASTSIM and KBTNH. The KBTNH is 7.8–51 times faster than FASTSIM working on 36–256 discretisation elements, respectively.

In the example of application, the KBTNH has been applied for curving simulations and results compared with those obtained with the creep force generator employing the elliptical regularisation. The results significantly differ, especially in predicted creepages, because the elliptical regularisation neglects generation of the longitudinal creep force by spin creepage.     

Exploiting floating car data for time-dependent Origin–Destination matrices estimation

The study evaluates the added value generated by estimating dynamic demand matrices by information gathered from Floating Car Data (FCD).

Firstly, adopting a large dataset of FCD collected in Rome, Italy, during May 2010, all the monitored trips on a specific district of the city (Eur district) have been collected and analysed in terms of (i) spatial and temporal distribution; (ii) actual route choices and travel times. The data analysis showed that demand data from FCD are usually not suitable to retrieve directly demand matrices, due to a strong dependence of this information from the penetration rate of the monitoring device. Instead, origin–destination travel times and route choice probabilities from FCD are a much more reliable and powerful information with respect to FCD origin–destination flows, since they represent the traffic conditions and behaviors that vehicles experiment along the path.

Thus, several synthetic experiments have been conducted adopting both travel times and route choice probabilities as additional information, with respect to standard link measurements, in the dynamic demand estimation problem. Results demonstrated the strength and robustness associated to these network based data, while link measurements alone are not able to define the real traffic pattern. Adopting both the information of origin–destination travel times and route choice probabilities during the demand estimation process, the spatial and temporal reliability of the estimated demand matrices consistently increases.     

A cluster analysis approach for differentiating transportation modes using Bluetooth sensor data

A variety of automatic data collection technologies have been used to gather road and highway system data. The majority of these automatic data collection technologies are designed to collect vehicle-based data and either do not have the capability to collect other travel mode data (e.g., bicycles and pedestrians), or may need to be deployed differently to support this capability.

One type of wireless-based data collection system that has been deployed recently is based on Bluetooth technology. A key feature of Bluetooth-based data collection systems that makes travel mode identification feasible is that the Bluetooth-enabled devices within vehicles are also present on bicyclists and pedestrians. This research explores the effectiveness of applying cluster analysis methods when processing data collected via Bluetooth technology from vehicles, bicyclists, and pedestrians to automatically identify the associated travel modes. The results of several experiments utilizing multiple Bluetooth-based data collection units arranged linearly and in relatively close proximity on a simulated intersection demonstrate the potential of cluster analysis to accurately differentiate transportation modes from the collected data.