In recent years adverse processes of suburbanization have been observed in cities. It has become a serious challenge for urban and transport planners, as it influences largely the quality of space, the quality of life, and the cost of running the city. This paper is dedicated to travel models in areas serviced by a railway system, and is based on a real-life survey example of the Błonie community, a district belonging to the Warsaw metropolitan area. Research carried out in 2014 focused on combined travels behaviors recorded using GPS locators as well as quantitative research (volumes of users across various transport systems).

Transport is one of the factors influencing the development of metropolitan areas. However, for its efficient work, numerous optimizations are required. Main tasks are shortening travel time, improving service quality and increasing the number of passengers served. The author has presented current studies on the field in optimization of public transport, mainly ways to optimize the transport network construction, based on large data sets about the population and their communication behaviour. Methods of combining various types of public transport with each other are presented. In the paper also are presents authors studies on the communication accessibility within the city of Cracow. Estimated distances from buildings to various types of public transport stops. The results were presented in aggregated form. Calculated communication speed of three types of public transport functioning in Cracow has also been discussed.

The public transport service is highly essential to meet the demand due to a rapidly growing population and mobility. Thus providing public service and improve its service becomes an urgent need in recent years. In Iraq, the Bus system represents the backbone in public transportation, which is based mainly on highway infrastructure. To meet the growing mobility needs, enhancing public service provided only by bus routes is essential. Measuring bus route performance represents one of the crucial transit research topics in the last recent years. The current study tries to investigate the urban public route's efficiency utilizing the "data envelopment analysis (DEA)" technique. To analyze route performance, DEA is using, and performance measures include route design, cost, service, operation, and comfort efficiency are selected and calculated for different routes. Efficiency and effectiveness are the output of this process. Bus company owners can also use the results of this study to improve their services, attract new customers, and better manage their resources.

This paper presents the nodal nature of park spaces in New York City in the context of the variant use of forms of traditional and alternative mobility in the city. Using comparative analysis of thematic maps and plans of mass and individual transportation, a review of planning documents and in-situ observations in the city area, it was demonstrated that New York parks have become efficient transportation channels for daily migrations within the dense urban fabric. The phenomenal plasticity of park areas in terms of the formation of and adaptation to new uses and the growing scale of the issue prompt a deeper analysis of the phenomenon.

All the available modes of travel and their respective travel parameters must be known to the commuters before their trip. Otherwise they may either spend more money or more time for the trip. In addition to this, recent pandemic, rapidly spreading novel corona virus is demanding a smart solution for contactless commuting. This paper suggests a practical solution to make both the above possible and it emphasizes the applicability of two developed android applications, one for travel data collection and another to predict travel time for a multimodal trip within the study area. If the whole trip is by a single mode, the user can get the corresponding travel time estimate from “Google maps”. But, if the trip is by multiple modes, it is not possible to get the total travel time estimate for the whole trip at a time from “Google maps”. A separate travel mode for “auto” is unavailable in “Google maps” alongside drive, two-wheeler, train or bus and walk alternatives. It is also observed that the travel time estimate of “Google maps” for the city buses is inaccurate. Hence, the two modes (Buses and Autos) were chosen for the study. Unless and until the travel times and stopping times of the two modes are known, it is not possible to predict their trip times. Hence, the mobility analysis was performed for the two modes in the study area to find their respective average travel rate at peak hours, across 15 corridors and the results were presented.