Transport system planning and modelling traffic demand in cities and / or urban areas oriented to the needs of the population are key to create and implement a safe, efficient, environmentally friendly and sustainable transport system. The trend of population growth in urban areas also carries with it an increase in the number of activities resulting in more trips made. By grouping population activities, it is possible to predict future transport demand. The classic approach to transport planning uses a four-step aggregate travel demand model. With the development of information and communication technologies, the possibilities for increasing the mobility of the population are further expanded. Due to the complexity of this problem, the classical approach does not provide the optimal solution. The new approach based on population activity allows for a more detailed and accurate representation of the habits of the population during different periods of the day and thus influences the number, type and characteristics of their travels. This paper will provide an overview of current research on predicting transport demand in urban areas, based on population activity through a disaggregated approach. The methodology, discussion of the topic, evaluation of future research on the subject matter and conclusion will also be presented.

Older primary schools in Croatia are frequently located in densely built older settlement cores. Micro-locations surrounding school entrances are often not the result of prior urban or traffic planning; instead, they are retroactively managed through infrastructure and signalling interventions. Pupils participate in traffic as pedestrians, cyclists, e-scooter users, or passengers in cars, school buses, or public buses. The proposed integrated research approach includes: an online survey of pupils’ travel behaviour, systematic safety assessments of entrance micro-locations using the iRAP methodology, as well as field measurements and in-depth analysis of vehicle speeds, traffic flow and structure. For classes organised in two shifts, an online survey of parents (for classroom-based education) and pupils (for subject-based education) covered 56% of the pupil population. Because pupils’ travel mode is the factor most susceptible to influence through infrastructure improvements, statistical analysis was conducted using the χ²-test for the purpose of investigating relationships with the other three traffic-relevant determinants: school age group, pupils’ sex, and distance from school. Approximately three-quarters of pupils live less than 2 km from the typical school. If peak vehicle traffic does not coincide with the peak of pupil arrivals and departures during the overlap of two school shifts, part of the traffic on the school-access street may be unrelated to direct school-access activities. Vehicle-type restrictions and one-way traffic operation should be considered as measures to improve pupils’ safety. The proposed groups of measures for improving pupils’ safety include: (i) educational workshops for pupils, parents and teachers; (ii) reconstruction of school entrance micro-locations; (iii) targeted interventions in the traffic environment within a 2 km perimeter.

Quality of service in urban surface transit has a great impact on sustainable urban mobility, with travel time being one of the most important indicators of service reliability. But urban surface transit is prone to many disturbance factors causing travel time discrepancy and variability, making transit less reliable for passengers. We conducted research in the City of Zagreb on a single tram line by splitting it into constant-frequency segments. The first phase was modeling minimum segment travel times to base the indicators and predictors upon, and the second phase was establishing correlation matrices between the predictors and travel time using Pearson correlation coefficients and significance. Variance inflation factors were used to check for collinearities. While predictors belonging to the transport supply irregularity group did not have an impact, the ones belonging to the disturbance factors group showed correlation, with six of them being significant. This research in rarely represented tram transit determined the most significant disturbance factors rich in traffic context that can be used to develop travel time prediction models.