1 . Cities across America have set up bike-share programs as sustainable (可持续的) transportation alternatives to ease traffic jams. Yet despite these programs’ advantages and popularity, significant operational challenges remain.

Residential neighborhoods face shortages of bicycles in the morning rush hour, while business districts have a lack of bikes in the evening. What’s more, parking docks can be full at certain hours when riders have reached their destinations. Currently, many systems rebalance their bike supply using box trucks that reallocate dozens of bikes at a given time. This is a costly, time-consuming attempt that enlarges the systems’ carbon footprint. Thus, it is vital that rebalancing be as efficient as possible. With that in mind, my colleagues and I set out to develop advanced algorithms (算法) to deal with the novel challenges of bike-share operations.

One area to which our methods were applied was the Bike Angels program that our collaborators run in cities across the country. Initially, riders of Bike Angels earn points by renting or returning bikes at certain high-need stations.

In a study, we found that these fixed patterns cause inefficiencies when returns are incentivized (激励) in locations where they do not actually help with rebalancing.

The changes to operational patterns we made based on our algorithms can increase the efficiency of the incentives by around 15 percent. And importantly, this form of rebalancing comes with a much better carbon footprint than box trucks!

Our algorithms are applied for other purposes, too. For instance, by analyzing usage information, we were able to help operators make accurate determinations about whether they need to send out crews to fix a broken bike or dock as well as dealing with maintenance (维修) issues in a timely manner.

To be sure, algorithms can’t fix every new transportation problem and they have many limitations. But what is clear is that many challenges that come with new options, including e-bikes and free-floating bike-share systems, require the real-time data-driven approaches.

1. What can we learn about bike-share programs in Paragraph 1?

A．They have many drawbacks.

B．They add a burden to city traffic.

C．They work much better in business districts.

D．They increase the popularity of sharing economy.

2. What does the underlined word “that” in Paragraph 2 probably refer to?

A．How to reduce box trucks’ carbon footprint.

B．How to use box trucks to reallocate bikes.

C．How to rebalance bike supply efficiently.

D．How to help riders earn more points.

3. Which of the following shows the advantages of the author’s algorithms?
①Increasing box-truck companies’ profit.
②Improving the effectiveness of incentives.
③Providing more incentives for regular cyclists.
④Making bike reallocation more environmentally friendly.
⑤Making the maintenance of bikes more timely and accurate.

A．①②③

B．①③④

C．③④⑤

D．②④⑤

4. What can be the suitable title for the text?

A．Creative approaches to easing traffic jams

B．Advantages of developing advanced algorithms

C．An introduction of Bike Angels’ incentive system

D．An effective solution to bike-share operational problems