Trip Fare Research Articles

Optimising ride-sharing efficiency: innovative shareability-focused pricing strategies

Ride-sharing is increasingly recognised as a potential solution to transportation issues such as traffic congestion and parking problems. The success of ride-sharing systems depends on the willingness of individuals to share rides, and effective pricing strategies are among the crucial factors. However, creating a fair pricing system for riders and providers remains challenging. This research introduces a new pricing scheme based on the concept of shareability. This concept considers all the possible sharing situations among trips. It is designed to incentivize riders and accommodate providers' criteria. This scheme calculates the trip fare for passengers, considering various ride-sharing scenarios derived from the shareability function, thus ensuring fairness and transparency for passengers. Using the macroscopic fundamental diagram, our model incorporates traffic dynamic conditions. We have applied the method to the real case of Lyon city in France to assess the effectiveness of our approach. Through a comprehensive analysis of various configurations of the proposed pricing scheme and benchmarking with the existing approaches, we demonstrate its superior performance in reducing ride rejections compared to traditional methods. This result showed the proposed method's potential for enhancing ride-sharing systems' effectiveness.

An incentive strategy for the retention of impatient passengers in ride-sourcing markets

A prominent problem plagued the ride-sourcing service providers is the instantaneous real-time demand–supply imbalance. This entails a terrible user experience and causes adverse sustainable results such as customer churn for the platform, which will damage both drivers’ income and the platform revenue. To reduce passenger cancellation caused by extended wait times, we propose an incentive-based queue management strategy —a discount method— to enhance the waiting experience and encourage passengers to stay in a post-peak period . We formulate the waiting process for a ride-sourcing service as an M/M/c+M queue system with impatient passengers, and model passengers’ reneging behaviors by characterizing the impact of discounted trip fare and updated queue information on their travel utilities. Based on such a behavior model, we can analyze the effect of the discount strategy on the queueing process, which allows us to maximize the platform’s profit growth by tailoring the discount strategy. Our result shows that the discount strategy can effectively increase the platform’s profit. Under the specific condition of the discount strategy, the earlier the strategy is implemented in the post-peak period, the higher platform profit can be achieved. Moreover, we explore the impact of queueing system parameters on the performance of the discount strategy and gain some interesting insights.

Microeconomic analysis of ridesourcing market regulation policies

We develop a microeconomic model of competition between ridesourcing, public transport, and cruising taxis to analyse how alternative ridesourcing regulation policies affect not only the ridesourcing service but also other modes of transport. To maximise social welfare in a framework with congestion externalities, we analyse three alternative regulation policies (i) a charge as a percentage of the ridesourcing trip fare (ii) a fixed charge per ridesourcing trip, and (iii) a fixed charge per ridesourcing vehicle. A randomised search algorithm is programmed and simulations are performed considering the base case of the Metropolitan Region of Santiago de Chile. We find that for the case of speeds below 9 km/h, the market power of the platform is dominated by the congestion externality effect, which leads to an optimum where the regulator charges a tax to the platform. In turn, waiting time for ridesourcing increases, but congestion decreases. Lower congestion scenarios lead to a ridesourcing subsidy, in which the regulation policy increases the number of ridesourcing drivers and reduces waiting time, at the cost of increasing congestion. For a given scenario (either low-congestion or high-congestion), we find that the profits of the agents involved (ridesourcing platform and drivers, taxi drivers, and bus operator) are sensitive to the type of regulation applied, however the total social benefit and consumer surplus are largely insensitive to the regulation adopted, therefore policymakers could take into account, for instance, the effects on the financial situation of the public transport sector when deciding which type of regulation instrument to apply to the ridesourcing sector.

Analisa Kelayakan Tarif Kereta Api Ekonomi Lokal Rute Sidoarjo - Bojonegoro dengan Metode Willingness to Pay

Feasibility Analysis of Local Economy Train Fares for Sidoarjo - Bojonegoro Route with Willingness To Pay Method. This study aims to determine the fare that is in accordance with the Local Economy Train service on the Sidoarjo - Bojonegoro route. The demand for train travel between the two cities is high due to the high economic potential and individual mobility in Sidoarjo and Bojonegoro regencies. However, the Local Economy Train service is still not optimal with air conditioning problems, long travel routes, and uncomfortable seating facilities. The willingness to pay method was used in this study by considering passenger load factors, distance fares, and passenger satisfaction. The evaluation was carried out quantitatively and qualitatively on the services and fares of the Local Economy Train for the Sidoarjo - Bojonegoro route. The load factor in 2021 reached 74.83%, exceeding the load factor standard of 70%. Factors that affect people's willingness to pay rates include punctuality, information, schedules, rates, facilities, service quality, cleanliness, security, and safety. The respondents' assessment shows that the facilities and services of the Local Economy Railway are good. The value of willingness to pay on a trip with a fare of IDR 6,000 is IDR 7,219.44, while with a fare of IDR 12,000 it reaches IDR 12,321.2. The trip fare is based on operational costs of Rp 10,773 for a distance of 133 km. This research is expected to help PT. KCI improves services and determines the appropriate fare for the Local Economy Train service on the Sidoarjo - Bojonegoro route.

Joint Econometric Model Framework for Transportation Network Company Users’ Trip Fare and Destination Choice Analysis

In this study, we examine the factors affecting Chicago, U.S., transportation network companies (TNCs) users’ trip fare and destination choice behavior. While trip fare has been examined from various perspectives, earlier fare models have not considered an exhaustive set of independent variables. Further, trip fare decisions are significantly influenced by trip destination. Therefore, in our study a joint model of trip fare and destination choice is proposed. The joint model system—linear regression for fare and multinomial logit model for destination—is developed based on Chicago TNC weekday trip data from January 2019 to December 2019. A wide range of origin- and destination-specific land use and built environment factors, transportation infrastructure attributes, and weather attributes were found to be significant in the model system. Based on log-likelihood and Bayesian information criterion measures, the model performance of the proposed joint model is found to be superior compared with independent fare and destination models. The applicability of our proposed fare and destination choice model is illustrated through fare prediction and destination elasticity analysis. The framework can potentially be employed to generate TNC fare for inclusion in level of service measures for TNC models in the mode choice model.

On-demand ride-sourcing markets with cryptocurrency-based fare-reward scheme

Cryptocurrency-based fare-reward scheme has been emerging from on-demand ride-sourcing platforms. The reward scheme provides each passenger with some units of cryptocurrency as rewards for use of ride service. The passengers can redeem the rewards for various redemption options. The redemption options offer the passengers economic benefits to offset a portion of costs of using ride services. In this way, the economic benefits serve as incentives for the passengers to repeatedly use ride services, which leads to increase in passenger demand for the platforms. From a passenger perspective, the reward scheme has a distinctive feature in that economic benefits can fluctuate according to changes in price of cryptocurrency in the future. Therefore, the passengers perceive the benefits differently based on their own risk preference and expectation of future price changes. It creates a challenge for the platforms because the optimal decisions for ride services vary by the value that the passengers place for their cryptocurrency rewards. To tackle this challenge, this paper analyzes the impacts of the reward scheme on on-demand ride-sourcing markets. The analysis is done with a mathematical model where a ride-sourcing platform makes decisions for trip fare, vehicle fleet size and cryptocurrency reward size. The reward decision is available under the assumption that the platform secures as many units of cryptocurrency as it rewards to passengers at their equilibrium price level. The model defines passengers’ perceived value of cryptocurrency rewards in terms of risk aversion level and expectation of future price growth of cryptocurrency. Afterwards, we investigate equilibrium and optimal properties of monopoly, social-optimum and second-best optimum ride-sourcing markets. Our analysis gives several key insights on the markets. First, the markets always locate at normal regime where interactions between passenger demand and vehicle supply for ride services converges to a stable equilibrium state. Second, this study shows that a ride-sourcing platform would make a decision to reward if passengers expect larger economic benefits from future redemption of cryptocurrency rewards than their current value. Third, it is shown that the quality of ride services would get worse because average pick-up time for passengers and drivers gets longer with more passenger retention. Fourth, this paper finds that the markets have more room to improve profit or social welfare as passengers expect larger speculative gains from reward redemption.

A ridesplitting market equilibrium model with utility-based compensation pricing

The paper develops a theoretic equilibrium model for ridesplitting markets with specific considerations of origin-destination demand patterns, competition with other transport modes, characteristics of en route matching, and spatial allocation of ridesplitting vehicles, to adequately portray the intertwined relationships between the endogenous variables and decisions. The operation property of the market under distance-based unified pricing is analyzed through the response of system performance indicators to the decisions. Moreover, a gradient descent algorithm is derived to find optimal operating strategies in the monopoly scenario and social optimum scenario. Leveraging the tight connection between trip’s utility and level of service (LoS), the paper then proposes a utility-based compensation pricing method to alleviate the inequity issue in ridesplitting, which results from the variance in waiting time and detour time and the implementation of unified pricing. Specifically, the trip fare of those with an initial utility smaller than a threshold will be compensated following a predefined compensation function. We compare its effectiveness and influence in different scenarios through numerical experiments at Munich. The results show that the proposed pricing method can improve the LoS and equity without losing any profit and welfare, and can even achieve increments in maximum profit and social welfare under certain conditions.

Coordinating supply and demand in ride-sourcing markets with pre-assigned pooling service and traffic congestion externality

This paper proposes an equilibrium model to describe a ride-sourcing market with both ride-pooling (RP) and non-pooling (NP) services considering traffic congestion externality. In particular, we develop a deductive model to approximate the pool-matching probability and passengers’ expected waiting time under a pre-assigned pool-matching mechanism with meeting points, which is being used in products like Uber Express Pool. Using the model, we derive the equilibrium properties and examine the optimal operating strategies for achieving two market scenarios, including the monopoly optimum and social optimum. In contrast to the traditional wisdom, we find that the market could exist in one or multiple equilibria, depending on the trip fare difference between RP and NP services. Additionally, we show that average sojourn time first decreases in the vehicle fleet size (when traffic congestion is mild) and then increases in the vehicle fleet size (when traffic congestion is heavy), exhibiting a “smiling curve”. It is found that the monopoly optimum solution is always located in the downward side of the “smiling curve”, which implies the ride-sourcing platform will self-regulate its fleet size to avoid heavy traffic congestion. We also discuss the impacts of potential labor supply and background traffic on the platform’s optimal operating strategy and its optimal profit. This study gains interesting managerial insights that help understand how ride-sourcing platforms determine optimal operating strategies for both RP and NP services by considering traffic congestion externality.

Ride acceptance behaviour of ride-sourcing drivers

The performance of ride-sourcing services such as Uber and Lyft is determined by the collective choices of individual drivers who are not only chauffeurs but private fleet providers. In such a context, ride-sourcing drivers are free to decide whether to accept or decline ride requests assigned by the ride-hailing platform. Drivers’ ride acceptance behaviour can significantly influence system performance in terms of riders’ waiting time (associated with the level of service), drivers’ occupation rate and idle time (related to drivers’ income), and platform revenue and reputation. Hence, it is of great importance to identify the underlying determinants of the ride acceptance behaviour of drivers. To this end, we collected a unique dataset from ride-sourcing drivers working in the United States and the Netherlands through a cross-sectional stated preference experiment designed based upon disparate information conveyed to the respondents. Using a choice modelling approach, we estimated the effects of various existing and hypothetical attributes influencing the ride acceptance choice. Employment status, experience level with the platform, and working shift are found to be the key individual-specific determinants. Part-time and beginning drivers who work on midweek days (Monday-Thursday) have a higher tendency to accept ride offers. Results also reveal that pickup time, which is the travel time between the driver’s location and the rider’s waiting spot, has a negative impact on ride acceptance. Moreover, the findings suggest that a guaranteed tip (i.e., the minimum amount of tip that is indicated upfront by the prospective rider, a feature that is currently not available) and an additional income due to surge pricing are valued noticeably higher than trip fare. The provided insights can be used to develop customised matching and pricing strategies to improve system efficiency. Since the study has been conducted during the COVID-19 crisis, the potential implications of the pandemic on ride acceptance behaviour have been examined using an Integrated Choice and Latent Variable (ICLV) model. The results show that drivers with a higher sensitivity to the COVID-19 effects tend to have a lower acceptance rate.

Characterizing the adoption and frequency of use of a pooled rides service

Pooled-ride services have a significant potential for reducing traffic externalities and enhancing transportation systems in the urban environment. These services and their users’ characteristics still need further inspection and exploration. We investigated factors encouraging the shift from the currently used modes to pooled-ride-services, the choice between different pooled services vehicles types, and the frequency of use of pooled-rides, using data collected via a large-scale online survey conducted in Mexico City, Mexico (CDMX) for a start-up that organizes pooled rides, Jetty. We modeled the pooled-ride-service adoption process as a function of the users’ sociodemographics, latent travel attitudes, accessibility to public transportation, trip characteristics, reasons to use the service, and users’ activities during the trips. We estimated hybrid choice models and binary logit models, which show that users’ sociodemographic and travel attitudes are the main factors impacting the shift from different modes to pooled rides. Service-related characteristics such as multi-tasking, trip fare, and avoiding parking problems also impact the shift decision. On the other hand, the frequency of service use is mainly impacted by trip characteristics such as total trip distance, and the headway at the user’s home location nearest Metro stations. Income, employment status, number of cars in the household, and gender were the only sociodemographic factors impacting the service use frequency directly and indirectly.

Optimal fare and fleet size regulation in a taxi/ride-sourcing market with congestion effects, emission externalities, and gasoline/electric vehicles

There have been various approaches to tackle vehicle emissions from a road congested network in pursuit of environmental sustainability. However, it is unclear how should the government set the trip fares and fleet sizes of a market with traditional street-hail taxis and the ride-sourcing service to maximize social benefit when both congestion effects and emission externalities are considered. Moreover, there have been real-world practices to reduce emissions from taxis and ride-sourcing vehicles by controlling the numbers of gasoline vehicles and electric vehicles. On one hand, some cities (e.g., Shenzhen, London) have launched a taxi replacement project that replaces all fossil-fueled taxis with electric taxis. On the other hand, the regulation preventing gasoline vehicles from being registered as ride-sourcing vehicles can also be observed. It remains a question of how should the social optimal fares and fleet sizes in a market with electric taxis and ride-sourcing vehicles be different from those in the market with gasoline vehicles. To address the above questions, this study investigates the optimal trip fare and fleet size regulation in an aggregate taxi/ride-sourcing market with congestion effects and environmental externalities. Two market scenarios are investigated, in which all taxis/ride-sourcing vehicles are gasoline vehicles or electric vehicles. We develop two social benefit maximization models and duopoly profit-maximization models, and derive the corresponding optimality conditions. Analytical and numerical results are given to gain insights into the regulation of taxi/ride-sourcing markets.

STUDY OF ECONOMIC CLASS PASSENGER RATES FOR PEOPLE TRANSPORTATION TRAVEL IN NORTH KALIMANTAN PROVINCE

<p>Establishing North Kalimantan as the 34<sup>th</sup> province in Indonesia with the provincial capital located in Tanjung Selor, Bulungan Regency, has influenced the pattern of transportation used by society between regions. Previously, most people traveled to Tarakan city by sea transportation because Tarakan is an island city and has a strategic role regionally as a National Activity Center (PKN) in North Kalimantan. Since January 2020, the North Kalimantan Transportation Service has permitted public transportation routes to Perum DAMRI Tanjung Selor Branch to serve three round-trips (pp) routes, namely: Tanjung Selor-Malinau (TGS-MLN), Tanjung Selor-Tana Tidung (TJS-KTT), and Malinau-Salang (MLN-SLG). This study aims to find the ideal fare for public transportation for people on the route, taking into account the Vehicle Operating Cost (VOC), the ability and willingness to pay (ATP and WTP) of DAMRI Bus users, as well as the user's willingness to pay. Pay more or WTP from Contingency Valuation (WTP-CV) if you get better service during the trip. The current single-trip DAMRI Bus fare is IDR 150,000.00 for the TJS-MLN/MLN-TJS route, the TJS-KTT/KTT-TJS route is IDR 100,000.00, and for the MLN-SLG/SLG-MLN route of IDR 50,000.00. The results of the study based on the VOC obtained one-time trip rates for each route, respectively: IDR 135,633.21, IDR 119,046.86 and IDR 35,186.00. The respondents' ATP analysis results obtained a one-time trip fare for each route in a row: IDR 142,994.05, IDR 119.747.47 and IDR 63.750.00. Meanwhile, respondents' WTP was obtained for each route: IDR 110,519.48, IDR 94,545.45, and IDR 39,107.14. The respondents' WTP-CV values were also obtained on each route, amounting to IDR 14,675.32, IDR 11,409.09, and IDR 8,303.57. Considering the captive rider as an economy class passenger and the benefits of the DAMRI Bus operator, the proposed tariffs for each route are IDR 130,000, IDR 110,000, and IDR 48,000.</p>

Impact of congestion charge and minimum wage on TNCs: A case study for San Francisco

This paper describes the impact on transportation network companies (TNCs) of the imposition of a congestion charge and a driver minimum wage. The impact is assessed using a market equilibrium model to calculate the changes in the number of passenger trips and trip fare, number of drivers employed, the TNC platform profit, the number of TNC vehicles, and city revenue. Two types of charges are considered: (a) a charge per TNC trip similar to an excise tax, and (b) a charge per vehicle operating hour (whether or not it has a passenger) similar to a road tax. Both charges reduce the number of TNC trips, but this reduction is limited by the wage floor, and the number of TNC vehicles reduced is not significant. The time-based charge is preferable to the trip-based charge since, by penalizing idle vehicle time, the former increases vehicle occupancy. In a case study for San Francisco, the time-based charge is found to be Pareto superior to the trip-based charge as it yields higher passenger surplus, higher platform profits, and higher tax revenue for the city.

Pricing and equilibrium in on-demand ride-pooling markets

With the recent rapid growth of technology-enabled mobility services, ride-sourcing platforms, such as Uber and DiDi, have launched commercial on-demand ride-pooling programs that allow drivers to serve more than one passenger request in each ride. Without requiring the prearrangement of trip schedules, these programs match on-demand passenger requests with vehicles that have vacant seats. Ride-pooling programs are expected to offer benefits for both individual passengers in the form of cost savings and for society in the form of traffic alleviation and emission reduction. In addition to some exogenous variables and environments for ride-sourcing market, such as city size and population density, three key decisions govern a platform's efficiency for ride-pooling services: trip fare, vehicle fleet size, and allowable detour time. An appropriate discounted fare attracts an adequate number of passengers for ride-pooling, and thus increases the successful pairing rate, while an appropriate allowable detour time prevents passengers from giving up ride-pooling service. This paper develops a mathematical model to elucidate the complex relationships between the variables and decisions involved in a ride-pooling market. We find that the monopoly optimum, social optimum and second-best solutions in both ride-pooling and non-pooling markets are always in a normal regime rather than the wild goose chase (WGC) regime—an inefficient equilibrium in which drivers spend substantial time on picking up passengers. Besides, in general, a unit decrease in trip fare in a ride-pooling market attracts more passengers than would a non-pooling market, because it not only directly increases passenger demand due to the negative price elasticity, but also reduces actual detour time, which in turn indirectly increases ride-pooling passenger demand. As a result, we prove that monopoly optimum, social optimum and second-best solution trip fares in a ride-pooling market are lower than that in a non-pooling market under certain conditions. These theoretical findings are further verified by a set of numerical studies.

Examining human attitudes toward shared mobility options and autonomous vehicles

This paper presents a comprehensive analysis of people’s attitudes toward shared mobility options and autonomous vehicles (AVs), with a focus on the underlying patterns and potential determinants. A stated preference (SP) survey was designed and implemented in the U.S. Four sets of questions were included in the questionnaire, each focused on one unique aspect of user attitudes, including a) preferences for mobility options and lifestyle (such as overall view of driving, factors in mode choice decisions and technology engagement), b) perceived benefits and concerns of shared mobility option, c) reasons toward or against private vehicle ownership, and d) motivations for and desired features of AVs. A structural equations model was developed to identify latent attitudinal factors and examine the correlations between the latent attitudes (as the endogenous variables) and the observed covariates (including the socio-economic and demographic characteristics, and users’ current mobility profile, such as mode use frequency, travel distance, and trip fare). The model identified eleven latent factors that represent various aspects of attitudes toward AVs and shared mobility options. The findings could be used by policymakers and Transportation Network Companies (TNCs) to a) recognize the users’ latent attitudes, b) understand the underlying patterns of attitudes, c) implement plans and policies more efficiently, d) guide or influence users’ perceptions, and e) enhance travel behavior models. This study lays the foundation for further analysis on understanding user acceptance and adoption of these emerging mobility options, which is essential to estimate the likelihood and magnitude of behavior shifts in the era of automated, connected and shared mobility.

CityLines: Designing Hybrid Hub-and-Spoke Transit System with Urban Big Data

Rapid urbanization has posed significant burden on urban transportation infrastructures. In today's cities, both private and public transits have clear limitations to fulfill passengers’ needs for quality of experience (QoE): Public transits operate along fixed routes with long wait time and total transit time; Private transits, such as taxis, private shuttles and ride-hailing services, provide point-to-point transits with high trip fare. In this paper, we propose CityLines , a transformative urban transit system, employing hybrid hub-and-spoke transit model with shared shuttles. Analogous to Airlines services, the proposed CityLines system routes urban trips among spokes through a few hubs or direct paths, with travel time as short as private transits and fare as low as public transits. CityLines allows both point-to-point connection to improve the passenger QoE, and hub-and-spoke connection to reduce the system operation cost. To evaluate the performance of CityLines, we conduct extensive data-driven experiments using one-month real-world trip demand data (from taxis, buses and subway trains) collected from Shenzhen, China. The results demonstrate that CityLines reduces 12.5-44 percent average travel time, and aggregates 8.5-32.6 percent more trips with ride-sharing over other implementation baselines.

Efficient Subsidies via Supply Re-usability

Surge pricing finds equilibrium prices in periods of excessive demand or scarce supply. Effective or not, it is a business risk when riders perceive spiking prices as exploitation of people's emergency. This paper studies subsidy policies that avoid the downside of surge pricing when accommodating the fluctuation of demand. We show that the re-usability of driver supply presents a hidden capacity. Tapping it wisely through supply-side subsidies prescribes a non-pricing alternative to the current pricing policies without the need for either hiking price or recruiting new drivers. We use a queueing model together with the Stackelberg game to analyze how to optimally subsidize a reusable pool of driver supply, both myopically and in the long run. Knowing drivers are self-interested and given a specific structure of base trip fare, our analysis shows that injecting a healthy dose of myopic subsidy into the matching process reverts unfavorable decisions of individual drivers. In aggregate, the induced supply multiplier effect significantly boosts vehicle circulation and effective demand. We also show that, on the other hand, the impact of myopic subsidies on long-run throughput is not monotonic. There is a physical limit in terms of how much the platform can tap this hidden capacity. However large the budget of incentive, its size is intrinsically constrained by the spatial structure of base trip fare and the distribution of customer travel distances.

Travel behavior and price preferences of bikesharing members and casual users: A Capital Bikeshare perspective

Even though casual users of bikeshare account for a large share of ridership and revenue at public bikeshare systems in North America, very little is known about the characteristics and preferences of casual users and how they compare to registered members. The primary objectives of the study include identifying the similarities and differences between members and casual users along demographics, usage and indicated preferences; and examining and modeling pricing preferences of bikeshare users. An intercept survey was conducted to obtain demographic information, bikeshare usage and various preferences of Capital Bikeshare users in the metro Washington DC area. The survey data was validated against the data from an existing member survey with large sample using goodness of fit tests. Survey participants reported that single trip fare (STF) and annual membership paid at once as their preferred pricing options and a combination of STF, 24-hour pass, and annual membership with monthly installments as their favorable pricing model. Logistic regression findings indicate that, when compared to casual users, registered members are more likely to be White, earn more and reside in the D.C. area. Casual users make fewer bikeshare trips and are less sensitive to the service (station density) compared to members. Gender, age and income distribution do not appear to influence casual fare product choice. Results from this study are useful in policy-making, planning and operations for bikeshare systems.

Pricing Schemes and Seller Fraud: Evidence from New York City Taxi Rides

In expert markets, different pricing schemes generate different financial incentives for sellers to defraud customers. Using rich microdata on New York City taxi rides, we examine the differences between trips of non-local passengers and those of comparable local passengers, and explore to what extent the differences in trip distance, duration and fare vary with drivers’ financial incentives. We demonstrate that, for trips subject to two-part tariff, the differences are higher when the variable rate is increased or when the post-dropoff occupancy is expected to be lower, and the impact of the post-dropoff occupancy is more pronounced when the variable rate is increased; however, for trips subject to flat-fare scheme, the differences are unaffected by the fare rate or the expected occupancy.

Measuring Overcrowding in Ahmedabad Buses: Costs and Policy Implications

Urban transport systems in India have been significantly stressed due to the rapid urbanization. The stress manifests as both external and internal. External stress is the on road congestion (as significant share of the public transport systems are road based) and internal congestion is the overcrowding of people within the public transport vehicle. This study aims to investigate the overcrowding in buses and discuss its policy implications using Ahmedabad as a case study. Further deterioration of urban transport systems occur due to lack of dedicated funding available to urban local bodies to improve its level of service. To tackle the problem of level of service, it is important to understand the utility associated with a public transport service. Traditionally, this has been measured using factors such as time and monetary cost of travel. However, the level of comfort of users is not accounted for. This study uses a well-established method of contingent valuation to identify costs associated with non-marketable goods such as a level of comfort in public transport system using the concept of willingness to pay. The findings indicate Ahmedabad has high levels of crowding in its bus systems. The average per trip fare (for the two services studied) ranges from Rs 9 to Rs 12. The generalised cost, (including value of time, accounting overcrowding) turned out to be Rs 34 to Rs 41, about 3.5 times the fare. Sensitivity analysis showed that this can be lowered by 20-25% if overcrowding is negligible. The implication is that reducing overcrowding will not only increase patronage and ‘image’ of the public transport system but will also bring down the economic costs to the society.