Urban Transit Ridership Research Articles

Post-pandemic Recovery of Transit Ridership and Revenue in Canada

Four years after the onset of the COVID-19 pandemic in March 2020, Canada’s public transit ridership and farebox revenues remain below pre-pandemic levels. Despite the lifting of mobility and assembly restrictions and significant population growth in the past two years due to an influx of immigrants, foreign students, and temporary workers, transit ridership and revenues have not recovered. The pandemic restrictions caused an 84 percent drop in ridership and farebox revenues. Despite the slow recovery, urban transit ridership in Canada is expected to reach the pre-pandemic level by 2026.

How wet is too wet? Modelling the influence of weather condition on urban transit ridership

Drawing on transit smart card data coupled with weather station records across a 12-month period, this paper investigates the influence of local weather on urban transit ridership in Brisbane, Australia. A set of negative binomial regression models is applied at both daily and half-hourly levels to approximate the weather–transit ridership relationship across different passenger types. In addition, combining genetic algorithm and partial correlation, a new analytic approach is developed to capture the critical points in the weather–transit ridership relationship. The modelling results reveal that the influence of weather condition on transit ridership is not consistent in terms of passenger type and transit mode, and such characterised influence also has an obvious character of spatiotemporal heterogeneity. At the daily level, senior is found to be more sensitive to variations in weather condition compared to other types of passengers. At the half-hourly level, weather is shown a stronger influence on transit ridership during midday off-peak hours vis-à-vis either morning peak or evening peak. Furthermore, grounded in the new analytic approach, our modelling results also highlight that there exist critical points when weather variable reaches transit ridership varies substantially and those critical points are differed by transit mode, trip origin and time period. We argue our findings would be important in enriching an evidence base with the capacity to inform transit operators to (re)schedule and (re)design their transit services in response to all weather conditions.

Impact of weather on urban transit ridership

Utilizing daily ridership data, literature has shown that adverse weather conditions have a negative impact on transit ridership and in turn, result in revenue loss for the transit agencies. This paper extends this discussion by using more detailed hourly ridership data to model the weather effects. For this purpose, the daily and hourly subway ridership from New York City Transit for the years 2010–2011 is utilized. The paper compares the weather impacts on ridership based on day of week and time of day combinations and further demonstrates that the weather’s impact on transit ridership varies based on the time period and location. The separation of ridership models based on time of day provides a deeper understanding of the relationship between trip purpose and weather for transit riders. The paper investigates the role of station characteristics such as weather protection, accessibility, proximity and the connecting bus services by developing models based on station types. The findings indicate substantial differences in the extent to which the daily and hourly models and the individual weather elements are able to explain the ridership variability and travel behavior of transit riders. By utilizing the time of day and station based models, the paper demonstrates the potential sources of weather impact on transit infrastructure, transit service and trip characteristics. The results suggest the development of specific policy measures which can help the transit agencies to mitigate the ridership differences due to adverse weather conditions.

Urban transit ridership in an energy supply shortage

The capabilities of a transit company to provide service is usually based on the existing commuter traffic. Rapid increases in commuter ridership will cause transit service to deteriorate. The transit companies' ability to respond to increased ridership is controlled by the length of travel by the new riders, the availability of additional buses and trained staff. This study found that under a situation where fuel was allowed to price itself in a free market, the transit company could face a situation of declining revenue per bus mile with increasing patronage. This comes about because those with longer commuter trips find it more advantageous to use transit. The transit patronage increase to be expected with a five per cent reduction in fuel supply is similar to that experienced by a doubling of fuel price. The more uniform change in the trips made throughout the urban area will probably not alter the average length of trip. The transit company should be able to keep the same ratio of passenger revenues to operating costs. The models used to estimate the changes in transit ridership appear to be generally applicable. The model used for the pricing scheme is similar to one used in the Tyneside-Wearside Transportation Study. The experience of Seattle's Transit System over the last year and a half tends to confirm the estimates found in the gasoline rationing model.